Epilepsy is a neurological illness characterized by recurring seizures, and with its early and accurate detection is crucial for effective care and patient outcomes can be improved. This goal of this research is to apply machine learning to design and assess an automated method for detecting epilepsy using Electroencephalogram (EEG) brain signals with help of machine learning. The main objective is to provide a dependable tool for diagnosing epilepsy from EEG data, supporting healthcare professionals in making timely interventions. We gathered a large collection of EEG recordings from individuals with and without epilepsy, involving a wide range of demographics and clinical characteristics. Various machine learning algorithms were employed to train and evaluate our automated system, including Support Vector Machines, Decision Trees, Random Forests, and K-nearest neighbors. Based on EEG readings, the results illustrate that machine learning systems are capable of diagnosing epilepsy. The system achieved high accuracy, sensitivity, and specificity, thus minimizing false positives and negatives. Furthermore, in order to evaluate the model’s generalization ability across various feature combinations, cross-validation was conducted. Moreover, we discuss the interpretability of the model’s predictions, addressing the need for transparency and trust in clinical decision support systems. Future work may incorporate additional clinical data sources, such as medical history and imaging, to enhance diagnostic accuracy. In conclusion, our automated method showcases the possible applications of machine learning in epilepsy detection, offering a valuable tool for medical practitioners and researchers.