This research focuses on designing an automated test bench for alternators, which incorporates real - time monitoring using Internet of Things (IoT) technologies, advanced electrical analysis, and intelligent diagnostics based on a Multilayer Perceptron (MLP ) artificial intelligence model. The system allows the testing of automotive alternators under various operating conditions, measuring the RPM, voltage, and current generated and sending the data to a web platform via an ESP32 microcontroller. Multiple tes ts were performed during the experiment at various load levels and speeds, demonstrating a direct relationship between voltage and RPM. Additionally, the PZEM - 003/017 achieved a measurement margin of error of less than 1%, and the AI model's fault detectio n accuracy exceeded 90%. Likewise, a finite element analysis (FEA) of the system's structural framework was performed, valida...