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Predicting chemotherapy response in metastatic colorectal cancer (mCRC) remains challenging due to the lack of reliable biomarkers. We developed an artificial intelligence (AI)-driven pathomics approach that integrates multicenter data and provides visual explanations to enhance interpretability. Using transfer learning and attention-based multiple instance learning (MIL), our deep learning algorithm automatically extracted predictive features from whole slide images (WSIs). The model was pretrained on The Cancer Genome Atlas (TCGA) to identify survival-related histopathological patterns and fine-tuned on a multicenter mCRC cohort to classify patients as chemotherapy-sensitive or resistant.Compared to a baseline model trained solely on mCRC data, our approach improved model stability, reduced variability, and increased predictive accuracy in the holdout test set achieving a nearly 15% improvement in area under the curve (AUC: 0.54 to 0.68). High attention-weighted tile visualizations highlighted tumor microenvironment features potentially linked to chemotherapy resistance, offering insights for both clinical decision-making and biological research.Clinical relevance- This study demonstrates the feasibility of using AI and digital pathology, through transfer learning, to enhance chemotherapy response prediction in mCRC. By improving interpretability and incorporating multicenter data, our approach offers insights that could inform treatment strategies. Although further refinement is needed for clinical deployment, this framework lays the foundation for integrating multi-omics data to achieve AI-powered personalized oncology care.