Cancer prevalence continues to rise globally, with significant implications for public health. Projections from GLOBOCAN reveal an alarming increase in cancer incidence and mortality rates, driven by aging, urbanization, and lifestyle changes. In 2020, there were approximately 19.3 million new cancer cases and 10 million deaths worldwide; moreover, these numbers are increasing substantially each year (Ferlay et al., 2021). This poses a considerable challenge in oncology, which highlights the need for innovative strategies to enhance patient outcomes, despite progress in early detection, treatment options, and supportive care. The rising incidence of cancer demonstrates the essential requirement for predictive tools that can assess disease progression, personalize treatments, and improve survival outcomes (Adeniran et al., 2024). Predictive modeling is essential in providing algorithms and methodologies to predict clinical outcomes using patient-specific data. These models are critical in oncology for prognosis assessment, high-risk patient identification, and therapeutic strategy optimization (Yu et al., 2024). Utilizing various data sources, including clinical records, imaging, genomics, and proteomics, predictive modeling enhances early diagnosis and supports personalized medicine (Coskun et al., 2024). These models are crucial as they offer actionable insights, allowing clinicians to make informed decisions that can greatly influence patient care and resource distribution.