As the Internet of Things (IoT) continues to grow in usage by 14% globally in 2025, it is only natural that it has paired with machine learning (ML). IoT captures what’s happening in the moment, and ML helps turn that information into useful insights. And as both technologies continue to evolve, their combined impact is only growing. 

In this blog, we will explore what makes the combination of IoT and ML so powerful, key ways in which machine learning is utilised in IoT, real-world applications of this combination, and how it benefits businesses worldwide. 

What makes IoT and ML a powerful combination?

IoT devices generate large quantities of data from their connected sensors, machines, vehicles, and everyday equipment. On its own, this data can be overwhelming to sort through. 

Instead, machine learning algorithms sift through these large, continuous data streams, spotting patterns, learning from them, and turning raw information into actionable insights.

As IoT devices feed updates continuously, machine learning models can process that information instantly, making it possible to automate decisions the moment conditions change. 

Here are some examples that show just how well Internet of Things and machine learning work together: 

• Predictive maintenance: Sensors track vibration, heat, pressure, and run-time data. ML models identify early warning signs and predict when a component is likely to fail, helping organisations schedule repairs before downtime occurs.
• Demand forecasting: Smart metres and connected devices provide real-time consumption data. Machine learning uses this alongside historical patterns, seasonality, and external factors to forecast demand with far greater accuracy.
• Quality control: Cameras and sensors on production lines capture images and measurements. ML models detect defects sometimes even before they’re visible to an employee.
• Energy optimisation: IoT-enabled buildings monitor lighting, heating, and equipment usage. Machine learning automatically adjusts settings to reduce waste and cut operational costs.
  
  

Key Machine Learning techniques used in IoT

Because IoT environments generate massive and continuous sensor data, ML techniques help detect patterns, make predictions, and automate decision-making without constant supervision. 

Here are the three most common approaches and how they’re typically used in IoT environments.

Supervised learning

Supervised learning is widely used in IoT for classification and prediction tasks. 

Machine learning algorithms, such as random forests and decision trees to build prediction models that require fewer resources in the long run. They help identify anomalies in sensor readings, categorise device behaviours, and forecast future outcomes like equipment failure. 

Predictive maintenance uses historical sensor data labelled with failure events to predict when equipment is likely to break down. Energy consumption forecasting involves learning from past usage data to estimate future demand. Fault detection classifies sensor readings as “normal” or “abnormal” to identify issues before they escalate.

Unsupervised learning

Unsupervised learning is equally important, especially when labelled data is scarce. 

Techniques such as k-means clustering, DBSCAN, and principal component analysis (PCA) help uncover hidden patterns and group similar behavioural profiles across sensor networks. 

These methods are crucial in applications like detecting unusual traffic in networked devices, segmenting usage patterns, or compressing high-dimensional data before transmission to reduce bandwidth demands.

Reinforcement learning

Reinforcement learning (RL) and edge-optimised ML are becoming increasingly important to upleveling IoT. It focuses on learning the best actions through trial and error, guided by rewards or penalties. 

RL allows systems to learn optimal actions through trial and feedback, making it highly effective for autonomous control, such as smart grid optimisation, adaptive traffic systems, or robot navigation. Edge ML techniques, including lightweight neural networks and model compression, enable real-time decision-making directly on low-power IoT devices, reducing latency and improving privacy. 

Real-world use cases across industries

The mix of IoT and machine learning is reshaping the way organisations run their day-to-day operations, enabling smarter automation, more accurate predictions, and smoother workflows.

Manufacturing: maintenance and quality control

Manufacturers rely on IoT sensors to monitor equipment conditions, tracking vibration, temperature, cycle times, and more. Machine learning models analyse this data to predict when Machines will need maintenance, helping prevent costly unplanned downtime. 

On production lines, computer vision systems paired with ML can detect defects early, improving product quality and reducing waste.

Utilities: grid management and energy forecasting

Utility providers use IoT-connected metres, transformers, and substations to gather real-time data from across the grid. 

Machine learning processes this information to forecast energy demand, identify anomalies, and optimise load distribution.

Logistics: route optimisation and asset tracking

In logistics, IoT devices track the location, condition, and movement of vehicles and goods. Machine learning helps optimise delivery routes based on traffic patterns, weather, and historical performance. 

It can also predict delays, suggest alternatives, and monitor the health of assets like shipping containers or vehicle components to ensure smooth operations.

Retail: personalisation and stock optimisation

Retailers leverage IoT-enabled devices, such as smart shelves, beacons, and connected checkout systems, to understand customer behaviour in real time. 

An example of this is the Co-op supermarket chain, as they are trialling AI-powered CCTV and motion sensors to detect suspicious customer behaviour in real time.

Machine learning uses this data to personalise recommendations, optimise store layouts, and anticipate what products shoppers will want next. Combined with inventory data, ML also helps retailers maintain optimal stock levels and reduce overstock or shortages.

The business benefits of IoT and ML

Reduce costs and downtime

Continuous monitoring and predictive insights help identify issues early, allowing businesses to prevent breakdowns, schedule maintenance proactively, and avoid unnecessary repair costs.

By implementing digital predictive maintenance, asset availability can be increased by 5% to 15% which would reduce maintenance costs by up to 25%. 

Improve efficiency and customer experience

Real-time data and automated decisions streamline operations, cut waste, and optimise resources. At the same time, Machine learning enables more personalised customer experiences, improving satisfaction and engagement.

A commonly known example of this is the Amazon Go stores. One opened up in London, UK, where customers were able to simply walk into the store, choose the items they wanted and walk out without having to go to a checkout point. Instead, they were automatically billed through their Amazon account for the items they took off the shelf, saving customers valuable time during their shopping experience. 

Enable smarter, faster decisions

Instant insights from IoT data allow teams to react quickly to changing conditions. Machine learning supports better decision-making by turning constant data streams into clear, actionable guidance.

Common challenges and how to overcome them

While IoT and machine learning offer huge potential, organisations often face hurdles when implementing these technologies. Framing these challenges as opportunities, IoT and ML themselves can provide solutions when applied thoughtfully.

Data complexity and scale

IoT devices generate massive amounts of diverse data, which can feel overwhelming. Machine learning thrives on this kind of information, turning raw, complex data streams into actionable insights. By leveraging ML, organisations can extract value from every data point rather than being bogged down by its volume.

Integration and legacy systems

IoT solutions are increasingly designed to work alongside legacy infrastructure, and modern MLOps practices streamline deployment, monitoring, and maintenance of Machine learning models. This ensures new insights enhance, rather than disrupt, existing operations.

Security and privacy

With so much data flowing across networks, security and privacy are natural concerns. IoT can actually strengthen protection by enabling real-time monitoring for anomalies, while secure edge computing keeps sensitive data closer to the source, reducing exposure to cyber threats.

The role of edge computing and MLOps

Edge computing allows data to be processed closer to where it’s generated, reducing latency and bandwidth needs. Combined with MLOps, a set of practices for managing ML lifecycle, organisations can deploy, monitor, and update models efficiently, making IoT and machine learning systems faster, more resilient, and more reliable.

By addressing these challenges proactively, businesses can unlock the full potential of IoT and machine learning, turning obstacles into opportunities for smarter, more efficient operations.

What’s next for IoT and Machine Learning?

Growth of AI and no-code tools

Running machine learning models directly on IoT devices reduces latency, cuts bandwidth usage, and enables instant, real-time decision-making. Instead of sending all data to the cloud, devices can analyse information locally and take immediate action, which is especially valuable in manufacturing, autonomous vehicles, and smart cities.

At the same time, no-code and low-code platforms are lowering the barrier to entry for IoT and ML. These tools allow organisations to build, deploy, and manage solutions without relying entirely on specialised technical teams. This means that even smaller companies or non-technical departments can harness IoT data and machine learning to solve real business problems quickly.

What we are seeing is that the organisations that are seeing the greatest impact from AI are the ones that aim to achieve more than just cost reductions as a result of implementing AI. 

80% of companies set efficiency as a main objective of their AI implementation, but the companies that are seeing the most value from AI often set growth or innovation as additional objectives.

Increasing accessibility for non-tech teams

As platforms become more intuitive, business analysts, operations teams, and other non-technical staff can access actionable insights directly from IoT systems. This democratisation of technology empowers more people to make data-driven decisions, optimise workflows, and contribute to innovation. It also fosters collaboration between IT, operations, and business teams, ensuring that IoT and ML initiatives are aligned with real organisational goals.

Potential for continuous learning and autonomous systems

IoT devices produce a constant stream of real-time data, creating the perfect environment for Machine learning models to continuously improve. This enables adaptive systems that evolve with changing conditions, like production lines that adjust automatically to fluctuations in demand or smart grids that dynamically balance energy loads.

Explore IoT solutions

The potential of IoT and ML is constantly expanding and evolving as these systems grow. Whether you’re looking to improve operational efficiency, reduce downtime, enhance customer experiences, or unlock new business opportunities, the right IoT solutions can help you turn data into meaningful results.

At Three Group Solutions, we help businesses unlock the full potential of IoT and machine learning. We provide the reliable connectivity and device-management tools you need to scale your IoT initiatives. Whether you’re deploying a handful of sensors or managing a fleet of connected devices worldwide, our tailored solutions make it easier to turn data into actionable insights and drive smarter, more efficient operations.

Learn more about IoT solutions today and how it can benefit your business.