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In the era of biometric technologies and artificial intelligence (AI), palmprint analysis has emerged as a promising tool not only for personal authentication but also for personality identification. Palmprints, containing unique line patterns, ridges, and geometric structures, are traditionally studied in palmistry to infer behavioral and psychological traits. Integrating deep learning with palmistry-based feature extraction allows for an innovative approach toward personality analysis that combines scientific methods with traditional knowledge. This project proposes a Personality Identification System using Deep Learning and Palmistry Features, where convolutional neural networks (CNNs) are trained on palmprint datasets to learn discriminative features. In parallel, traditional palmistry attributes such as heart line, head line, life line, and mount analysis are incorporated to enrich the personality prediction process. The fusion of AI-based feature learning and palmistry features provides a hybrid model for robust personality trait classification. The system's performance is evaluated using metrics such as classification accuracy, interpretability, and reliability, aiming to bridge the gap between biometric science and traditional palmistry. The system utilizes convolutional neural networks (CNNs) to automatically learn discriminative palmprint features, including ridge patterns, line intersections, and texture variations. In parallel, palmistry attributes such as heart line, head line, life line, and mount structures are quantified and digitized for feature-level fusion. A deep neural network (DNN) classifier then predicts personality traits such as emotional stability, intellect, and leadership ability