Abstract
This systematic review article investigated machine learning strategies and algorithms designed to enhance real-time data processing in dynamic and big data environments. As data complexity and volume increased, the need for scalable, adaptable, and computationally efficient solutions became more pressing. Despite significant advances, gaps persisted in handling heterogeneous data sources, ensuring real-time adaptability, and addressing privacy concerns. This article systematically assessed the effectiveness of machine learning models, including supervised, unsupervised, and reinforcement learning, across various dynamic systems. Based on the findings from the literature review, a possible framework was developed, outlining critical processes involved in real-time machine learning, such as data ingestion, feature engineering, model inference, and continuous monitoring. The framework demonstrated methods to improve scalability and performance using cloud computing, edge solutions, and automated model retraining. The findings suggested that emerging trends such as federated learning, ethical AI, and neurosymbolic AI offered promising solutions to unresolved challenges. This article highlighted the importance of continued research to develop robust, secure, and adaptable machine learning frameworks capable of meeting the demands of real-time, high-velocity data processing in increasingly complex environments.