AI-Driven Predictive Analytics for Autonomous Systems: A Machine Learning Approach

Abstract

The integration of AI-driven predictive analytics in autonomous systems has revolutionized the way machines learn and respond to dynamic environments, enabling enhanced decision-making capabilities. This paper explores a machine learning approach to predictive analytics, focusing on the ability of autonomous systems to forecast operational challenges and optimize performance in real-time. Leveraging supervised and unsupervised learning algorithms, the study demonstrates how predictive models can be trained to anticipate system failures, optimize resource allocation, and enhance overall system resilience. The proposed framework addresses key challenges in data collection, model training, and real-time analytics, offering solutions for improving the accuracy and efficiency of predictive models. Applications in autonomous vehicles, robotics, and industrial automation are discussed, showcasing the role of machine learning in driving next-generation autonomy. Results indicate significant improvements in system reliability, operational efficiency, and proactive fault mitigation, making this approach critical for future autonomous systems.