Two-wheeled vehicles present significant stability challenges, particularly at low or zero velocity. In this paper, we present the design, implementation, and validation of a sensor-integrated, self-balancing electric motorcycle prototype that addresses this issue. The system is architected around the inverted pendulum model, employing a proportional-integral-derivative controller for longitudinal (pitch) stability and a novel counter-steering strategy for lateral (roll) stability. A sensor-based attitude sensing module, built around an inertial measurement unit (IMU), continuously detects the motorcycle’s dynamic state. The IMU data are processed through a Kalman filter to provide a robust and accurate estimation of the vehicle’s tilt angle by effectively fusing noisy accelerometer measurements with drifting gyroscope signals. This enhanced state-detection process ensures reliable real-time feedback for control. The entire system is managed by a STMicroelectronics32 microcontroller, demonstrating that advanced sensing, detection, and control algorithms can be efficiently implemented on open-source hardware. Experimental results validate the controller’s effectiveness, showing rapid balance recovery (within 1–2 s) from external disturbances of up to ±5 degrees with less than 15% overshoot (equivalent to ±0.75 degrees). In this work, we not only provide a practical implementation of a self-balancing sensing-driven control system but also offer a valuable and replicable platform for further research in intelligent transportation, sensor-based mobility systems, and robotics education.