Face recognition (FR) is a captivating and dynamic area of study within the discipline of computer vision, with significant implications for real-time applications. Despite persistent research efforts, the field of face recognition remains in a continuous state of development. Recent advancements have demonstrated promising results but have also introduced notable challenges. These challenges arise from diverse factors, including variations in face orientations, fluctuating degrees of illumination, blurred features, and the complexities introduced by facial alterations through surgical procedures. Employing sophisticated face detection and recognition techniques, the technology can effectively and accurately identify individuals from collected facial images or video footage. This work seeks to provide an exhaustive analysis of the body of existing research on face detection and identification, highlighting their weaknesses and proposing a solid model to address them. We aim to build a sophisticated face recognition system that can overcome current challenges and improve accuracy by comprehending the state-of-the-art approaches and examining their strengths and drawbacks. The proposed model delivered outstanding performance indicators, such as a precision 95.65%, recall 94.12%, an F-score 95.70%, and an accuracy 97.06%, respectively. By exploring the challenges encountered in uncontrolled environments, this study seeks to shed light on the factors impacting the system’s efficacy. Moreover, it delves into existing methodologies and potential resolutions, envisioned to mitigate these challenges and bolster the overall efficiency of facial recognition systems.