ARTIFICIAL INTELLIGENCE FOR MATHEMATICIANS: HARNESSING MACHINE LEARNING AND DATA SCIENCE FOR MATHEMATICAL DISCOVERY AND PROBLEM-SOLVING Chagas,J.F., Ph.D Catapure Project- Water Disaster Relief First Baptist Conroe, Conroe, Texas, USA Abstract This paper explores the transformative potential of artificial intelligence (AI) in the field of mathematics, emphasizing the application of machine learning (ML) and data science techniques to enhance mathematical discovery and problem-solving. AI-driven approaches enable mathematicians to leverage vast computational resources and data-driven insights, opening new avenues for research and pushing the boundaries of mathematical knowledge. The paper provides an overview of key AI concepts and methods relevant to mathematics, including symbolic reasoning, theorem proving, numerical optimization, and pattern recognition. It also discusses the challenges and opportunities associated with the integration of AI into mathematical research workflows, such as data representation, model interpretability, and collaboration between mathematicians and AI experts. The paper presents case studies and practical examples demonstrating the successful application of AI in various subfields of mathematics, such as number theory, geometry, and dynamical systems. It concludes with a vision for the future of AI- driven mathematics, emphasizing the need for interdisciplinary education, open science, and responsible innovation. 1. Introduction 1.1 The Role of Artificial Intelligence in Mathematics Artificial intelligence (AI) is revolutionizing the way we approach complex problems and discover new knowledge across various scientific domains. In the field of mathematics, AI-driven approaches have the potential to transform the way we explore abstract structures, prove theorems, and solve challenging problems, complementing and augmenting human intuition and creativity (De Raedt et al. 2018). The application of AI in mathematics builds upon the long-standing tradition of using computational methods and symbolic reasoning to advance mathematical research. However, the recent advancements in machine learning (ML), data science, and high-performance computing have significantly expanded the scope and capabilities of AI in mathematics, enabling the analysis of vast datasets, the discovery of hidden patterns, and the automation of complex reasoning tasks (Bajaj et al. 2019). ML algorithms can learn from large collections of mathematical objects and structures, such as numbers, equations, graphs, and manifolds, to identify patterns, relationships, and trends