Predictive analytics is pivotal for data-driven decision-making in diverse fields, including finance, healthcare, and urban planning. This paper investigates the optimization of predictive analytics through advanced machine learning techniques. We examine various machine learning models, including regression, classification, and neural networks, and assess their performance using a range of optimization strategies. Key methods discussed include Gradient Descent, Adam optimization, and regularization techniques such as L1 and L2. Our study demonstrates that these optimization approaches significantly enhance model accuracy and generalization. Additionally, we explore hyperparameter tuning methods, such as Grid Search, Random Search, and Bayesian Optimization, to identify optimal configurations for predictive models. The results indicate substantial improvements in predictive performance and offer practical insights for model selection and implementation. Future research directions include exploring sophisticated optimization algorithms and integrating domain-specific adaptations to further refine predictive models. This work provides a comprehensive framework for optimizing predictive analytics, contributing to more accurate and reliable data-driven decision-making.