Ensuring candidate authenticity is critical in large-scale public recruitment. The Staff Selection Commission (SSC) often faces challenges due to outdated or manipulated photographs, leading to verification delays and impersonation risks. Manual checks are inefficient at scale. This paper presents a real-time, AI-driven system combining face detection, liveness detection, and image quality assessment to validate photos during submission. The proposed system employs a lightweight deep learning model trained on a diverse dataset of over 25 million images, achieving high accuracy in rejecting poor-quality, obstructed, or tampered inputs. By integrating advanced image preprocessing techniques and real-time validation on both web and mobile platforms, the framework ensures seamless user experience while maintaining strict security standards. This auto- mated approach significantly reduces the burden of manual verification and helps mitigate risks of identity fraud during recruitment. The solution not only enhances verification reliability for SSC but also establishes a scalable model that can be adopted across various government and private sector recruitment processes, setting a precedent for secure, automated identity validation across domains.