Epilepsy is a neurological illness that presents considerable obstacles for individuals due to the unpredictable nature of seizures, which can occur at any time, including while sleeping. Because of its unpredictable nature, there is a higher chance of injury, disturbed sleep, and in extreme situations, sudden unexplained death from epilepsy (SUDEP). Therefore, reducing these risks and enhancing the quality of life for those with epilepsy depends on early seizure onset diagnosis. This study investigates several machine learning strategies targeted at the early identification of seizure start and looks at ways to stop seizures in people with epilepsy. Analysis of electroencephalogram (EEG) data from people with epilepsy diagnoses is the main goal of this research project. In order to recognize patterns that point to imminent seizures, we have created sophisticated machine learning algorithms. By using labeled EEG data from patients, these algorithms are trained to recognize and understand distinguishing characteristics linked to pre-seizure states. Our research demonstrates the potential of machine learning methods to improve seizure detection early on, potentially improving patient outcomes. Apart from examining machine learning uses, we introduce a new headband EEG technology that records brain activity using dry electrodes. This technology offers better comfort and usability while exhibiting signal quality that is comparable to conventional scalp-based EEG techniques. A Bio Amp EXG tablet, dry EEG electrodes, and an ESP32 microcontroller with Wi-Fi connectivity are all included with the headband. This design is appropriate for continuous monitoring in a variety of situations since it provides onboard data processing and real-time streaming of raw EEG data. This device’s main function is to continuously record EEG signals in order to identify and categorize epileptic seizures. Pre-processing removes noise and artifacts from the EEG data that the headband collects, improving the overall signal quality. After that, the EEG data is classified using auto-encoder methods, which also help to determine the beginning of seizures. The technology notifies caretakers via a smartphone application when it detects a seizure, guaranteeing that prompt aid may be given. Additionally, we have created a cloud-based platform that enables remote access to EEG data for medical experts. Better communication between patients and their healthcare providers is facilitated by this feature, which also supports efficient treatment planning and monitoring. This project showcases a number of significant advancements in the realm of managing epilepsy. Through the integration of cutting-edge EEG acquisition technology with machine learning techniques, we offer a comprehensive solution for early seizure identification and patient assistance. Our project aims to create a wearable headband that is simple, comfortable, and non-intrusive, allowing people with epilepsy to go about their daily lives without disruption. This headband is designed for continuous monitoring of seizure activity. By detecting early signs of a seizure, it can send emergency alerts to family members, coworkers, or bystanders via a mobile app, sounding an alarm to ensure that help is quickly available. This allows others to administer anti-seizure medication or seek immediate medical assistance, preventing escalation to more severe seizures that could lead to life-threatening situations if left untreated. The goal of combining cutting-edge data analysis, intuitive technology, and remote monitoring features is to enhance the quality of life for people who have epilepsy.