This thesis develops a pressure monitoring system for the Cessna 172’s pump-fed fuel setup, targeting faults such as leaks, blockages, contamination, pump starvation, and pressure drops. The aim is not only to detect these problems early but also to apply targeted responses based on the fault type. A pressure sensor continuously monitors the fuel line; its analog signal is converted digitally by a built-in Analog to Digital Converter, which operates in parallel with the existing approved path. This ensures that there is no interference. The digital stream passes through SCADA and Rawan layers before reaching a decision module. An AI engine acting as co pilot analyses the data, learns from it, classifies faults, and triggers corrective actions or suggests corrective steps to the pilot. A model of fuel system dynamics is modelled in MATLAB/Simulink using Bernoulli and Darcy-Weisbach equations to address both normal and faulted operation. Performance analysis indicates the ability to detect faults early with a high degree of accuracy in pressure drift detection to within 0.1% of range in under 30 seconds. This paper will also cover economic study for future implementation as well as examines environmental and safety implications related to fuel-system health monitoring. Recommendations are provided for extending the system to certification pathways and cockpit integration in general aviation aircraft.