CRC continues to be a major threat to global health, responsible for the majority of cancer-related morbidity and mortality. Although effective, traditional histopathological methods are limited by the lack of objectivity, time constraints, and inter-observer variability, which indicates a need for automated diagnostic solutions. Recent advances in deep learning, especially in convolutional neural networks (CNNs), have been shown to have the remarkable potential to improve accuracy in disease detection and classification tasks. Automated systems may improve early CRC detection, reduce diagnostic discrepancies, and minimize the workload of pathologists to make scalable applications in clinical practice. This article presents a new multiclass classification method using advanced CNN architectures like EfficientNetB0 and hybrid models. The dataset is 5,880 histopathological images, classified into eight classes of tumor and normal tissues. Preprocessing of the data includes resizing, normalization, and augmentation to increase the robustness of the model. The framework used for the implementation and training of models was TensorFlow. Among all the tested models, the architecture with the highest classification accuracy of 99.57% was EfficientNetB0. This architecture proved to be very reliable and precise in CRC-type identification. Hybrid models also showed high accuracy and further validated their efficacy in hierarchical feature extraction. These experimental findings emphasize the revolutionary role of artificial intelligence in the diagnosis of cancer and open doors to improved patient outcomes and personalized treatment strategies.