- Cataract is a leading cause of blindness worldwide, and surgical intervention remains the primary treatment. Accurate prediction of surgical outcomes can significantly enhance patient care and reduce postoperative complications. This paper presents a deep learning-based predictive analytics model for cataract surgery outcomes using advanced image processing techniques. The proposed framework integrates Generative Adversarial Networks (GANs), Contrast Limited Adaptive Histogram Equalization (CLAHE), and Genetic Algorithms to refine preoperative imaging and enhance prediction accuracy. A comprehensive dataset of preoperative and postoperative images is used to train a convolutional neural network (CNN)-based predictive model. The effectiveness of the model is evaluated using standard metrics such as accuracy, precision, recall, and F1-score. The proposed approach demonstrates superior performance in predicting surgical outcomes compared to traditional statistical models. This research highlights the potential of AI-driven predictive analytics in ophthalmology, paving the way for more personalized and effective treatment plans.