Effective treatment for colorectal cancer relies on its early detection, which requires advanced methods to enhance its accuracy and dependability. This study presents an optimized Deep Belief Network (DBN) framework for colorectal cancer diagnosis, employing a hybrid Pigeon-Inspired Optimization (PIO) and Differential Evolution (DE) algorithm. The technique makes use of hierarchical feature extraction properties of DBNs for capturing complex patterns from medical images. Although the PIO algorithm is introduced to obtain a more accurate result following pigeon homing behavior, its ability in optimization is coupled with DE, which is renowned for its strong optimization skills, to optimize the operation of DBN. This fusion method not only enables to optimally tune the hyperparameters and weights of DBN, also ensures both an accurate exploration as well utilization of search space. The method begins by pre-processing the input medical image, and then uses the DBN feature extraction. Next, the network is optimized by the hybrid PIO-DE method, improving the classification accuracy. Finally, the images are classified into cancerous and non-cancerous tissues using the optimized DBN. The proposed strategy, which makes use of the TCGA-COAD dataset, guarantees more robust reference system for training and validation in DBN to enhance detection sensitivity as well as clinical utility. The outcomes of the experiments showcase the enhanced effectiveness of the suggested approach, attaining higher levels of accuracy and efficiency in contrast to traditional techniques.