This study looks into how artificial intelligence (AI) models can be used to track and understand brain activity by analyzing electroencephalogram (EEG) signals. Since EEG signals are a non-invasive way to measure brain activity, they can provide important information about neurological conditions and cognitive states. The proposed methodology involves a comprehensive pipeline, starting with the collection and preprocessing of EEG data, followed by feature extraction and the application of advanced AI models for accurate analysis. The preprocessing stage includes noise removal, artifact rejection, and segmentation of EEG data into meaningful epochs. Feature extraction focuses on capturing essential characteristics, such as power spectral density and temporal dependencies, providing a rich set of inputs for subsequent AI model training. Selecting a model entails deciding which architecture—convolutional neural networks (CNNs), recurrent neural networks (RNNs), or hybrid models—will best process the spatial and temporal information contained in the EEG signals. The AI models are trained on annotated datasets, leveraging advanced optimization techniques to learn intricate patterns indicative of different cognitive states. Performance evaluation is conducted using standard metrics, ensuring the model's accuracy, precision, recall, and generalization capability. Interpretability measures, such as visualizing relevant features and creating saliency maps, enhance the transparency of the AI model's decision-making process.