Advances on Mathematical Modeling and Optimization with Its Applications discusses optimization, equality, and inequality constraints and their application in the versatile optimizing domain. It further covers non-linear optimization methods such as global optimization, and gradient-based non-linear optimization, and their applications. Math has become a part of our everyday life. From the minute we wake up to the moment we go to bed, we use mathematics in every aspect of our lives. The exhaustive contribution of mathematical aspects and applicative knowledge has become research worthy due to the invention and discoveries of many advanced sorts of theorems, Whether a person is Data Scientist, Data Analyst, or Machine Learning Engineer, “Mathematics” should be a key focus of the studies. Artificial Intelligence (AI) and Machine Learning (ML) are hot topics in the computer world, and for good reason. They help companies streamline operations and unearth data so that they can make better business decisions. The business growth is directly or indirectly managed by the proper trend analysis and the predictive approach of growth to the ultimate level of development. They’re boosting almost every industry by allowing employees to work more effectively, and they’re fast becoming a critical piece of technology for businesses to remain competitive with others. Python is the most popular computer program for applying mathematical and statistical methods to AI development and applications. Therefore, we have demonstrated the applications of Python in different applications of mathematics in AI in this book.