eng Oriental Scientific Publishing Company Biosciences Biotechnology Research Asia 0973-1245 2025-03-25 22 1 95 102 10.13005/bbra/3343 54134 article Naga Theja 1 Chaitanya 2 Vani Pushpa 3 Sudheendra 4 Santosh Kumar 5 Sambangi Satyananda Siva 6 Department of Medical Radiology and Imaging Technology, Joy University, Vadakankulam, Tamil Nadu, India. Department of Anesthesia, Centurion University of Technology and Management, Vizianagaram, Andhra Pradesh, India Department of Radio Diagnosis, Fathima Institute of Medical Sciences, Andhra pradesh India. Department of Radiology and Imaging Technology, Reva University, Yelahanka, Bengaluru, Karnataka, India. Department of Radiology and Imaging Technology, Maulana Azad National Urdu University Hyderabad, Telangana, India Department of Radiology, MNR University, Telangana, India By increasing production, effectiveness, and precise diagnosis, machine learning (ML) is revolutionizing the radiation therapy industry. In order to identify anomalies in different types of imaging including CT, MRI, and X-rays, this paper examines developments in machine learning (ML) approaches, especially convolutional neural networks (CNNs) and deep learning. These advancements have a great deal of promise for automation picture processing, lowering human error, and offering prompt, dependable diagnostic assistance. The requirement for sizable, exceptional datasets, the difficulties of technique validation, including ethical worries about privacy of patient information are some of the obstacles to the integration of machine learning (ML) in radiology. For ML to be widely adopted and its transformational promise in radiological imaging to be realized, these obstacles must be overcome. https://www.biotech-asia.org/vol22no1/exploring-the-frontiers-of-machine-learning-in-radiology-a-comprehensive-review-of-applications-advancements-and-the-challenges-that-lie-ahead/ CT; Machine Learning; MRI; Medical Diagnosis; X-RAY