Machine Learning (ML) is the engine of AI in fleet management.

Rather than following programmed rules, an ML model analyzes thousands of past trips, maintenance reports, and incident logs to discover correlations.

For example, it can 'learn' that the combination of a certain truck type, a specific driving style (measured by the accelerometer), and use on hilly roads leads to premature brake wear with 85% certainty.

Armed with this learning, the system can then generate a predictive maintenance alert for another truck exhibiting the same characteristics.

Similarly, it can create much more nuanced driver safety scores than a simple count of speeding events, by weighting events based on their context (time, weather, road type).

It is this ability to learn and adapt that makes ML so powerful for fleet analytics.