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Statistics and mathematics assist as the keystone of artificial intelligence, machine learning, data analysis and data science. Probability theory, linear algebra and optimization are fundamental enabling techniques for understanding uncertainty, manipulating data and optimizing algorithms. In AI and ML, probability, linear algebra and optimization techniques are support models, while in data analysis and data science, statistical methods facilitate descriptive, inferential analyses, regression and time series analysis. Collectively, these disciplines provide the theoretical framework and analytical tools essential for extracting insights, building models and making data-driven decisions. Statistics is a branch of mathematics that deals with collection, analysis, interpretation, presentation and organization of data. Statistical methods are used to analyze and interpret data to make inferences about populations from samples. Statistical methods are widely used in AI to improve performance and accuracy. A key component of data science is math. Mathematics contains various branches like algebra, geometry, Trigonometry, Calculus, Statistics and Probability. Developing machine learning algorithms requires an understanding of concepts from statistics, probability theory, calculus and linear algebra. These algorithms recognize patterns, forecast outcomes and categorize data using mathematical equations and functions. All Machine Learning algorithms are built on a mathematical foundation. These algorithms assist us in extracting information from the data. We write these algorithms in a programming language (typically libraries are available) and the computer machine that executes them on the data set seems to be intelligent, thus the title Artificial Intelligence. Machine learning is an integral part of artificial intelligence, which is used to design algorithms based on the data trends and historical relationships between data. Machine learning (ML) is defined as a discipline of artificial intelligence (AI) that provides machines the ability to automatically learn from data and past experiences to identify patterns and make predictions with minimal human intervention. This paper aims to explore role of the Statistics and Mathematics in Artificial Intelligence, Machine Learning, Data Analysis and Data Science. Also some factors are discussed like challenges, limitations and future directions.