Open Access

REVIEW

The Convergence of Computational Fluid Dynamics and Machine Learning in Oncology: A Review

Wan Mohd Faizal^1,2,*, Nurul Musfirah Mazlan^1,*, Shazril Imran Shaukat^3,4, Chu Yee Khor², Ab Hadi Mohd Haidiezul², Abdul Khadir Mohamad Syafiq²

1 School of Aerospace Engineering, Universiti Sains Malaysia, Engineering Campus, Nibong Tebal, 14300, Malaysia
2 Faculty of Mechanical Engineering & Technology, Universiti Malaysia Perlis, Arau, Perlis, 02600, Malaysia
3 Biomedical Imaging Department/Oncology and Radiotherapy Unit, Advanced Medical & Dental Institute, Universiti Sains Malaysia, Gelugor, 13200, Malaysia
4 Oncology Unit, Pantai Hospital Sungai Petani, Kedah, 08000, Malaysia

* Corresponding Authors: Wan Mohd Faizal. Email: ; Nurul Musfirah Mazlan. Email:

Computer Modeling in Engineering & Sciences 2025, 144(2), 1335-1369. https://doi.org/10.32604/cmes.2025.068660

Received 03 June 2025; Accepted 05 August 2025; Issue published 31 August 2025

Abstract

Conventional oncology faces challenges such as suboptimal drug delivery, tumor heterogeneity, and therapeutic resistance, indicating a need for more personalized, and mechanistically grounded and predictive treatment strategies. This review explores the convergence of Computational Fluid Dynamics (CFD) and Machine Learning (ML) as an integrated framework to address these issues in modern cancer therapy. The paper discusses recent advancements where CFD models simulate complex tumor microenvironmental conditions, like interstitial fluid pressure (IFP) and drug perfusion, and ML enhances simulation workflows, automates image-based segmentation, and enhances predictive accuracy. The synergy between CFD and ML improves scalability and enables patient-specific treatment planning. Methodologically, it covers multi-scale modeling approaches, nanotherapeutic simulations, imaging integration, and emerging AI-driven frameworks. The paper identifies gaps in current applications, including the need for robust clinical validation, real-time model adaptability, and ethical data integration. Future directions suggest that CFD–ML hybrids could serve as digital twins for tumor evolution, offering insights for adaptive therapies. The review advocates for a computationally augmented oncology ecosystem that combines biological complexity with engineering precision for next-generation cancer care.

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