This thesis focuses on the advancements in machine learning algorithms specifically designed for real-time data processing applications. The research aims to address the challenges associated with processing large volumes of data in real time, such as latency, accuracy, and computational efficiency. By evaluating various machine learning models and techniques, this study seeks to identify the most effective approaches for optimizing real-time data processing. The thesis emphasizes three core areas: the development of novel algorithms, the implementation of these algorithms in practical applications, and the analysis of their performance. Through a multidisciplinary approach that integrates computer engineering, data science, and artificial intelligence, this research provides insights into the future of real-time data processing and its potential impact on various industries. The findings contribute to the ongoing discourse on improving the efficiency and effectiveness of machine learning systems in dynamic environments.