This study proposes a low-cost intelligent spoon to mitigate hand tremors in Parkinson’s patients during eating, addressing the limitations of existing solutions such as high cost and limited adaptability. The system integrates a hybrid control framework combining a Long Short-Term Memory (LSTM) neural network and a Proportional-Integral-Derivative (PID) controller. An ESP32-based embedded platform equipped with an MPU6050 inertial sensor captures real-time tremor data, while a brushless motor dynamically adjusts the spoon’s position to counteract undesired movements. The LSTM model, trained on patient-specific tremor datasets, predicts future tremor patterns and provides feedforward compensation to the PID controller, reducing response latency and steady-state error by 32% compared to conventional PID-only systems. Experimental trials involving simulated and patient-volunteer tests demonstrated a 58.4% tremor suppression rate, with food spillage reduced by 62.5% when the system was activated. Notably, the prototype achieves a material cost of only 12.95 USD, representing 4.3–6.7% of the price of commercial alternatives (e.g., Lift ware Steady at 195 USD). Furthermore, IoT-enabled data logging and visualization allow continuous model refinement and clinical monitoring, enabling personalized rehabilitation strategies. These results validate the feasibility of AI-embedded low-cost assistive devices, offering a scalable approach to improve the quality of life for Parkinson’s patients while reducing healthcare disparities.