Introduction: Computed tomography (CT) is integral to oncology imaging, yet repeated scans increase cumulative radiation risks. This systematic review evaluates the comparative effectiveness of deep learning-based image reconstruction (DLR) versus adaptive iterative reconstruction (AIR) in reducing radiation dose and enhancing diagnostic image quality in oncology patients undergoing frequent CT imaging. Methods: A systematic search of PubMed, EMBASE, Cochrane CENTRAL, Elsevier, and Google Scholar was conducted for studies published between January 2020 and April 2025. Eligible studies included oncology patients undergoing CT using DLR or AIR techniques. Study designs included experimental, observational, phantom, and systematic reviews. Data was extracted on radiation dose metrics, image quality scores, contrast-to-noise ratio (CNR), and lesion detectability. Methodological quality was assessed using appropriate risk-of-bias tools. Results: Hundred studies met inclusion criteria. DLR consistently outperformed AIR in noise suppression, image clarity, and lesion detectability while achieving radiation dose reductions up to 75%. High-strength DLR algorithms (e.g., DLR-H, AiCE) showed superior performance in enhancing CNR and SNR. DLR maintained diagnostic accuracy across various anatomical regions and dose levels. Comparative studies confirmed DLR's clinical utility, especially in oncology imaging requiring frequent follow-ups. Conclusions: DLR significantly improves CT image quality and enables greater radiation dose reduction compared to AIR techniques. Its integration into oncologic imaging protocols supports safer, more effective imaging with high diagnostic precision. Standardization, long-term outcome evaluation, and integration with multimodal imaging are recommended to optimize DLR’s clinical adoption.