Facial recognition (FR) is a rapidly evolving field within computer vision, yet it continues to face significant challenges that impact its effectiveness and reliability. This research addresses critical issues such as pose variations, varying lighting conditions, occlusions, and inherent biases that affect the performance of facial recognition systems. By leveraging advanced machine learning techniques, particularly deep learning algorithms like Convolutional Neural Networks (CNNs), this study aims to enhance the accuracy and robustness of these systems. The proposed solutions are validated through extensive experiments, demonstrating improved performance in recognizing and detecting faces under diverse and challenging conditions. This paper also explores methods to mitigate biases and ensure privacy, contributing to developing more reliable and ethically sound facial recognition technologies. The findings provide a foundation for future advancements in the field, aiming to overcome the current limitations and expand the practical applications of facial recognition systems.