Colorectal cancer remains one of the most prevalent malignancies worldwide, with liver metastases occurring in nearly half of all patients and serving as the leading cause of cancer-related mortality in this population. The integration of artificial intelligence into clinical oncology has opened new avenues for improving both diagnostic accuracy and patient outcomes in colorectal cancer management. This review explores the evolving role of AI technologies in colorectal cancer care, with specific focus on their application in detecting and managing hepatic metastases. Current machine learning algorithms, particularly those employing deep neural networks, have shown remarkable capability in analyzing medical imaging data across multiple modalities. These systems can identify subtle patterns in CT scans, MRI sequences, and other imaging studies that may escape human detection, potentially enabling earlier diagnosis of metastatic disease. Several studies have documented AI performance that rivals experienced oncologists and radiologists in diagnostic accuracy, while offering the additional benefits of consistent interpretation and reduced analysis time. Radiomics approaches, which extract quantitative features from medical images, have demonstrated particular effectiveness in predicting which patients are most likely to develop liver metastases, allowing for more targeted surveillance strategies. The clinical implications extend beyond diagnosis to treatment planning and prognosis prediction. AI models are increasingly being developed to assist in therapeutic decision-making and to estimate patient survival outcomes based on imaging characteristics and clinical variables. However, widespread clinical adoption faces several obstacles, including the need for larger validation studies, standardization of algorithms across different healthcare systems, and addressing concerns about algorithm transparency and reliability. As cancer incidence continues to rise globally, particularly in resource-limited settings, AI technologies represent a promising tool to enhance care quality and accessibility. While these systems are intended to augment rather than replace clinical expertise, their potential to improve early detection and personalized treatment approaches positions them as valuable assets in modern cancer care.