Advances in Applications of Artificial Intelligence for Predictive Modeling in Applied Sciences

Abstract

Recent advances in artificial intelligence (AI) have significantly transformed predictive modeling across a wide spectrum of applied sciences. This review explores the integration of AI techniques such as machine learning, deep learning, and hybrid models into predictive frameworks within disciplines including environmental science, engineering, healthcare, and materials science. These methods have demonstrated superior capabilities in handling complex, nonlinear, and high-dimensional data, enabling more accurate forecasts and decision-making tools. The paper highlights key developments in AI-driven algorithms, including neural networks, ensemble models, and reinforcement learning, while also examining their application in real-world scenarios like climate modeling, disease prediction, and structural integrity analysis. Emphasis is placed on the shift from traditional statistical approaches to data-driven methodologies that leverage big data and real-time analytics. Additionally, the review addresses current challenges such as model interpretability, data quality, and computational costs, and outlines future research directions focused on explainable AI, model generalization, and interdisciplinary integration. Overall, this paper provides a comprehensive overview of how AI is revolutionizing predictive modeling in applied sciences, fostering innovations that are shaping the future of scientific discovery and technological advancement.