Muhammad Fahad Shinwari, Muhamad Zahim Sujod, Norhafidzah binti Mohd Saad, Sheeraz Iqbal, Junaid Ali, Islam Ahmed
Artificial intelligence (AI), together with modern machine learning (ML) and deep learning (DL), is reshaping research and practice across science, engineering, business, and everyday life. As scientific instruments and simulations generate increasingly large, high‑throughput datasets, ML methods have become essential for extracting patterns, organizing information, forecasting outcomes, and supporting evidence‑based decisions. This rewritten overview surveys how AI is being developed and applied across foundational disciplines, including information science, mathematics, medical science, materials science, geoscience, life sciences, physics, and chemistry. For each area, it highlights major bottlenecks (such as data scarcity, noisy observations, complex dynamics, and expensive experimentation) and explains how AI tools can help address them. It also outlines emerging research directions that deepen the integration of AI into scientific workflows. The goal is to provide a broad, readable guide to where AI can accelerate fundamental research and what practical challenges still need attention.