Brain-Computer Interface (BCI) technology represents one of the most transformative developments in biomedical engineering, enabling direct communication between the human brain and external devices. Electroencephalography (EEG) is the most widely used non-invasive technique for acquiring brain activity signals due to its safety, portability, and cost-effectiveness. EEG signals contain valuable information regarding cognitive processes, motor intentions, emotional states, and neurological conditions. However, EEG signal classification remains a significant challenge because of the non-stationary nature of brain signals, low signal-to-noise ratio, inter-subject variability, and high-dimensional feature spaces. Artificial Intelligence (AI) and Deep Learning have emerged as powerful solutions for improving EEG classification accuracy and enabling robust Brain-Computer Interface systems. This paper presents a comprehensive study of EEG Signal Classification for Brain-Computer Interfaces and proposes an Artificial Intelligence-Based EEG Classification Framework (AI-ECF) designed to enhance neural signal interpretation and BCI performance. The proposed framework integrates signal preprocessing, feature extraction, deep learning-based classification, and intelligent decision-making mechanisms. Experimental evaluations demonstrate significant improvements in classification accuracy, response time, and computational efficiency compared with traditional EEG analysis methods. The findings indicate that AI-driven EEG classification technologies will play a critical role in future neuroengineering, assistive technologies, healthcare systems, and intelligent human-machine interaction platforms.