Natural products (NPs) have long been recognized for their therapeutic potential, especially in cancer treatment, due to an ability to interact with multiple cellular pathways. The identification of molecular targets for NPs is a critical step in understanding anticancer mechanisms, with chemical proteomics emerging as a powerful approach. Both label-based and -free proteomic techniques have been utilized to identify these targets, each with their own advantages and limitations. While label-based methods provide high specificity through chemical tagging, the requirement for labeling can be a limitation, potentially altering NP natural properties. Conversely, label-free techniques allow for the detection of NP-protein interactions without structural modification but may struggle with transient interactions or low-abundance targets. Recent advances in artificial intelligence (AI) have further enhanced the field by improving target prediction and streamlining data analysis. AI-driven models, especially machine learning algorithms, have proven effective in processing complex proteomic data and predicting potential NP-protein interactions. The integration of AI with chemical proteomics accelerates target identification and deepens our understanding of the molecular mechanisms underlying the anticancer effects of NPs. This review explores the application of chemical proteomics and AI in the identification of cancer-related targets for NPs, highlighting current challenges and future directions for clinical translation.