The purpose of this study was to comprehensively analyze existing optimization algorithms for Machine Learning (ML) models and develop new approaches aimed at improving their performance and efficiency. The study compared traditional and novel machine learning optimization techniques to evaluate their impact on model performance. The main results include a detailed overview of the main optimization methods in ML, including gradient descent, stochastic gradient descent, metaheuristic-based methods, and non-zero methods. Specific cases of using optimization algorithms in ML tasks, such as image processing, machine translation, and speech recognition were presented. A table comparing the advantages and disadvantages of the methods by key performance metrics is provided. The structural diagrams and principles of operation of each method are presented. In addition, the methods of integrating the developed approaches into existing ML platforms are investigated. The study's results demonstrate that integrating novel optimization techniques significantly enhances machine learning model performance. These methods offer a substantial improvement over traditional techniques like gradient descent, providing greater flexibility and efficiency in handling complex and evolving data. The findings suggest that combining these approaches with existing optimization strategies can lead to more robust and scalable machine learning systems across diverse industries. The findings suggest that combining these methods with traditional approaches can enhance machine learning performance and guide future AI developments. The novelty of the research is in the introduction of the novel techniques like adaptive model selection and dynamic parameter adaptation to improve machine learning efficiency in real-time data environments.