Deep Vein Thrombosis (DVT) is the formation of blood clots in the lower limb because of prolonged immobility. Such critical medical conditions can be avoided by regularly using compression cuffs on the limbs; however, traditional compression devices lack adaptability, cause patient discomfort, and have inconsistent pressure application. This study presents a smart wearable and portable compression system integrated with sensors to receive real-time feedback. A DC motor, controlled by an H-bridge converter, inflates the system’s inflatable sleeves. The DC motor speed controls the pumping pressure, and a solenoid valve controls the inflation rate. Pressure, temperature, and moisture sensors are embedded in the inner part of the cuff to monitor the physiological parameters. An Arduino-based control system was used to control the inflation rate, air pressure, and duration of compression, optimally ensuring patient comfort. The designed pump was tested and shown adaptability when the sensor data changes. An AI-based control framework is also proposed in this work to enhance the performance and to make the pump autonomous and user-friendly. The response of the proposed AI-based control was validated through simulations of the model developed from fundamentals. The simulation results suggest that the AI-based DVT pump is more adaptable to the physiological parameter variations, even when the parameters change rapidly. The AI-driven model provides faster and more precise control of inflation and deflation patterns, preventing overheating, over-compression, and sweating. This study highlights the feasibility of a smart, wearable DVT pump that can adapt to the compression requirements while ensuring safety and comfort.