| Sumario: | Simple Summary: Colorectal cancer, which affects the large intestine, is the second leading cause of cancer deaths worldwide, making it crucial to develop better treatments. However, researchers face a major challenge: the experimental models they use in laboratories to study this disease and test new treatments often do not accurately represent what happens in real patients. This comprehensive review examines the various laboratory methods scientists use to study colorectal cancer, including growing cancer cells in flat dishes, creating three-dimensional mini-tumors from patient samples, and testing treatments in laboratory mice. The study found that traditional flat cell cultures are convenient and inexpensive but fail to capture the complexity of actual tumors. Three-dimensional models and mini-tumors grown from patient tissue better mimic real cancer behavior, while animal studies provide valuable insights despite species differences. Importantly, no single experimental approach perfectly represents human colorectal cancer. The research concludes that combining multiple experimental methods provides the most comprehensive understanding of the disease. This work will help scientists choose the most appropriate laboratory models for their research, ultimately leading to more effective treatments that could save countless lives and improve outcomes for colorectal cancer patients worldwide. Background/Objectives: Colorectal cancer (CRC) remains a major global health burden, marked by complex tumor–microenvironment interactions, genetic heterogeneity, and varied treatment responses. Effective preclinical models are essential for dissecting CRC biology and guiding personalized therapeutic strategies. This review aims to critically evaluate current experimental CRC models, assessing their translational relevance, limitations, and potential for integration into precision oncology. Methods: A systematic literature search was conducted across PubMed, Scopus, and Web of Science, focusing on studies employing defined in vitro, in vivo, and emerging integrative CRC models. Studies were included based on experimental rigor and relevance to therapeutic or mechanistic investigation. Models were compared based on molecular fidelity, tumorigenic capacity, immune interactions, and predictive utility. Results: CRC models were classified into in vitro (2D cell lines, spheroids, patient-derived organoids), in vivo (murine, zebrafish, porcine, canine), and integrative platforms (tumor-on-chip systems, humanized mice, AI-augmented simulations). Traditional models offer accessibility and mechanistic insight, while advanced systems better mimic human tumor complexity, immune landscapes, and treatment response. Tumor-on-chip and AI-driven models show promise in simulating dynamic tumor behavior and predicting clinical outcomes. Cross-platform integration enhances translational validity and enables iterative model refinement. Conclusions: Strategic deployment of complementary CRC models is critical for advancing translational research. This review provides a roadmap for aligning model capabilities with specific research goals, advocating for integrated, patient-relevant systems to improve therapeutic development. Enhancing model fidelity and interoperability is key to accelerating the bench-to-bedside translation in colorectal cancer care.
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