Epilepsy affects around 50 million people globally and is marked by unpredictable seizures due to abnormal neural activities in the brain. With nearly 30% of epilepsy patients experiencing drug-resistant seizures, there is a crucial need for effective seizure detection. This paper focuses on the challenge of predicting seizures to enable preventive actions. To achieve this the proposed system utilizes the STM32F103C8T6 microcontroller to process data from a 3-axis accelerometer, heart rate sensor and temperature sensor. The collected data is analysed using a fuzzy logic algorithm in MATLAB to interpret sensor readings for identifying seizures. When a possible seizure is detected, notifications are sent through GSM along with the patient’s location provided by GPS. Additionally, the system explores seizure detection methods device’s role in health monitoring and IoT integration in healthcare, with an accuracy rate of 94%. This work contributes to the field of healthcare technology by offering an innovative solution for continuous and automated monitoring of epilepsy patients, ultimately aiming to improve patient safety and quality of life.