Colon cancer remains a significant global health challenge, contributing to high morbidity and mortality rates. Accurate diagnosis through histological analysis is critical for effective treatment and improved patient outcomes. In this study, we present ColoNet, a convolutional neural network (CNN)-based system designed to enhance the early detection and classification of colon adenocarcinoma using LC25000 dataset comprising 10,000 digital histopathology images. Unlike conventional CNN-based models, ColoNet integrates an optimized feature extraction strategy with deeper convolutional layers, and dropout regularization, leading to improved generalization and reduced overfitting. Additionally, the proposed model achieves faster convergence and superior classification performance compared to existing methods. The system addresses the unique challenges in distinguishing benign from malignant conditions, automating the diagnostic process and streamlining colon cancer assessments for pathologists. ColoNet was rigorously evaluated across key performance metrics, including recall, accuracy, precision, and F1-score, achieving a maximum accuracy of 96.66%. This surpasses several state-of-the-art CNN models in colon cancer classification, demonstrating its effectiveness. Its high accuracy and robust classification capabilities make it a reliable tool for identifying different colon cancer stages. By providing an efficient and automated solution for pathologists, ColoNet is expected to significantly enhance colon cancer diagnosis, supporting early detection and staging, ultimately leading to better treatment outcomes and reduced cancer-related mortality. This research underscores the importance of AI-driven systems in transforming the landscape of digital pathology and improving clinical decision-making for colon cancer.