Artificial Intelligence (AI) has revolutionized healthcare and other sectors by finding new ways to solve problems and making a lot of tasks easier. The need for precise and timely disease prediction and monitoring, especially for neurological disorders like epilepsy, demand solutions that are more sophisticated than traditional ones. Signals from electroencephalograms (EEGs) contain vital information regarding brain functioning, but are intricate and noisy, making them difficult to analyze appropriately with traditional methods. In order to fix these shortcomings, we incorporated a variety of application-driven techniques, such as deep learning (DL) algorithms with Convolutional Neural Network (CNN) architectures or Long Short Term Memory (LSTM) networks for abnormal brain pattern detection, noise filtering and feature capturing through neural autoencoders, and transfer learning in which models developed in one domain are reused in another, allowing for effective predictions in the presence of insufficient data. Furthermore, additional accuracy was obtained by using hybrid models that integrated artificial intelligence (AI) models with traditional signal processing approached based on the usage of wavelet transformers. The results were profound. The DL model reached an accuracy of 95% for seizure detection, noise reduction with autoencoders reached 30%, transfer learning reduced training time by 40% and still maintained over 90% prediction accuracy, and hybrid models enhanced detection of subtle neurological events by 10%. This article provides a well prediction process for EEG patient detection which employed for real time monitoring system.