Purpose <p>Cancer remains one of the most terrible global health challenges, demanding innovative and efficient diagnostic methodologies. The purpose of the review is to explore the potential of computational intelligence has emerged as a promising tool in cancer diagnosis, emphasizing advanced algorithms, including machine learning as a subset of artificial intelligence, in analyzing complex datasets for accurate and timely detection.</p> Methods <p>Techniques such as neural networks, genetic algorithms, fuzzy logic, and evolutionary computing are used to analyse complex biological datasets, including genomics, proteomics, imaging, and clinical information. These advanced, data-driven models enable the identification of intricate patterns and potential biomarkers that traditional diagnostic methods may overlook. By integrating diverse and large-scale datasets, computational biology enhances the accuracy, speed, and personalization of cancer diagnostics, aligning with the goals of precision medicine to deliver targeted, patient-specific care and improve clinical outcomes.</p> Results <p>This review bridges the gap between computational innovations and clinical applications, offering researchers, and clinicians a comprehensive resource to understand emerging tools, evaluate their clinical utility, and recognize the ethical and data challenges involved. It also provides a critical platform for summarizing advances, comparing methodologies, and proposing frameworks that can accelerate translational research in precision oncology.</p> Conclusion <p>Computational intelligence in cancer diagnosis improved accuracy, speed, and personalized care through integration of clinical and biological data. By bridging computational tools with clinical practice, it helps researchers and clinicians toward advancing precision medicine and overcoming challenges in applying AI to cancer diagnostics.</p>