Integrating artificial intelligence (AI) and advanced imaging technologies in medical diagnostics is revolutionizing brain tumor recurrence prediction. This study aims to develop a precise prognosis model following Gamma Knife radiation therapy by utilizing state-of-the-art architectures such as EfficientNetV2 and Vision Transformers (ViTs), alongside transfer learning. The research identifies complex patterns and features in brain tumor images by leveraging pre-trained models on large-scale image datasets, enabling more accurate and reliable recurrence predictions. EfficientNetV2 and Vision Transformers (ViTs) produced prediction accuracy of 98.1% and 94.85%, respectively. The study’s comprehensive development lifecycle includes dataset collection, preparation, model training, and evaluation, with rigorous testing to ensure performance and clinical relevance. Successful implementation of the proposed model will significantly enhance clinical decision-making, providing critical insights into patient prognosis and treatment strategies. By improving the prediction of tumor recurrence, this research advances neuro-oncology, enhances patient outcomes, and personalizes treatment plans. This approach enhances training efficiency and generalization to unseen data, ultimately increasing the clinical utility of the predictive model in real-world healthcare settings.