Colon cancer remains one of the leading causes of cancer-related deaths worldwide, primarily due to late-stage diagnosis and disease heterogeneity. Recent advances in artificial intelligence (AI) have introduced promising solutions for improving colon cancer management, from early detection to personalized treatment planning. This review examines the current applications of AI technologies, particularly machine learning (ML) and deep learning (DL), in clinical practice. Analysis of recent studies reveals significant achievements in diagnostic accuracy, with AI systems demonstrating sensitivity rates of 91.6–98% and specificity rates of 83.4–95.2% in polyp detection. Key innovations include convolutional neural networks (CNNs) for real-time endoscopic analysis and multi-omics data integration for patient risk stratification. However, challenges such as data privacy, algorithmic transparency, and implementation costs remain significant barriers to widespread adoption. This review emphasizes the transformative potential of AI in colon cancer care while highlighting the importance of addressing implementation challenges through interdisciplinary collaboration and standardized validation protocols.