One of the key challenges in land registry and property management is ensuring the accuracy, transparency, and fairness of property transactions. Traditional land valuation and verification methods often rely on manual assessments, which can be subjective and prone to inefficiencies. This study explores the integration of artificial intelligence (AI), machine learning and computer vision to enhance the reliability of land registry processes through emotion recognition and behavioral analysis. Traditional land registration systems face challenges in ensuring transparency, fairness, and fraud prevention due to reliance on subjective human verification. This study proposes an AI-based framework that enhances land registry processes through facial expression analysis, behavioral tracking, and anomaly detection. The approach leverages deep learning models, including CNNs, RNNs, and OpenPose, combined with blockchain-based data storage for tamper-proof documentation. By identifying distress, coercion, or deception using facial and bodily cues, the system provides an additional layer of verification beyond biometric or document-based methods. Experimental results demonstrate significant improvements in fraud detection accuracy and real-time transaction validation. This novel application of AI fosters greater trust and modernization in land governance systems. Results indicate that unconscious emotional signals provide valuable insights into the fairness and legitimacy of land dealings, offering a data-driven alternative to traditional survey-based methods. This AI-powered approach aims to revolutionize land management by reducing fraud, improving verification accuracy, and ensuring a transparent, unbiased, and efficient property registration process. By incorporating emotion recognition technology, land administration can move towards a more automated and reliable framework, fostering trust and confidence in property ownership.