<p>The quality of the input pictures is the primary factor influencing palmprint recognition systems. However, inadequate lighting, auditory disturbances, and variations in contrast might render identification uncertain. We present a new method for enhancing palmprint images called Multiscale Contrast-Limited Enhancement for Adaptive Palmprints (M-CLEAP). With the use of multiscale processing and dynamic equalizing of the histograms and contrast limitations, this technique successfully reduces noise and enhances palmprint features. We introduce VisionInceptNet, an advanced model architecture that integrates the optimal features of Vision Transformer (ViT) and InceptionV3 network. VisionInceptNet exhibits the highest identification accuracy; however, extensive evaluation on prevalent palmprint datasets demonstrates that the proposed M-CLEAP significantly enhances image quality. VisionInceptNet and M-CLEAP collaborate to establish a robust and effective framework for palmprint recognition that surpasses all existing deep learning frameworks and conventional approaches. This collaboration enhances the scalability and reliability of palmprint-based authentication in practical applications such as forensic investigation, secure access management, and identity verification.</p>