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Traditional cargo fire detection systems rely on fixed thresholds, such as a specific temperature, to trigger alarms. However, these static approaches are prone to false positives and may miss slow-developing fires. This patented system introduces a novel method that evaluates sensor data dynamically, based on both spatial location and temporal evolution. Temperature sensors distributed throughout the cargo compartment generate continuous data streams which are analyzed over defined time windows. The system compares sensor values across locations and over time, identifying anomalies that indicate real fire events rather than harmless fluctuations. Configurable parameters allow operators to adapt detection sensitivity to different operational conditions, cargo types, and environmental variables. This method supports early detection of fires in Class C cargo compartments, including those inaccessible during flight. It is designed to integrate with aircraft health monitoring and fire suppression systems, and it can be deployed in both new aircraft designs and as a retrofit to existing platforms equipped with sensor arrays.