This research investigates the integration of Internet of Things (IoT)-driven neurostimulation treatment with Proximal Policy Optimization (PPO) for tailored management of neurological conditions. IoT gadgets, including wearable neurostimulators and real-time physiological sensors, incessantly monitor critical metrics like cerebral activity, heart rate, and muscular reaction. PPO, a Reinforcement Learning (RL) system, refines neurostimulation settings by modifying treatment according to individualized patient input. The system underwent assessment in a clinical study with 50 patients suffering from chronic neurological disorders, during which the algorithm was optimized to enhance treatment results while reducing adverse effects. The results indicated a 25% enhancement in symptom alleviation relative to traditional therapy approaches. Moreover, patient compliance improved by 30%, and adverse effects decreased by 15% because of the personalized modifications implemented by the PPO algorithm. The results illustrate the potential of IoT-based neurostimulation therapy, driven by PPO, to improve treatment efficacy and provide a more personalized, patient-centric strategy in the management of neurological illnesses. This method is poised to transform personalized healthcare, enhancing patient outcomes and treatment efficacy. © 2025 IEEE.