The ever-changing cost of airfare has always proved to be a significant barrier for travelers trying to make the most of their savings by conducting manual searches and scheduling reservations for specific times. However, with the advent of machine learning, there came into existence a practical way of enhancing the aviation sector’s capacity for decision-making and outcome prediction. Various regression methods such as CatBoost, Random Forest, etc. for precisely forecasting flight expenses with the help of machine learning approaches are studied in this chapter. We have examined various machine learning models and their ability to represent the complex relationships between a variety of factors that influence airfare, such as airlines, dates of travel, locations of origin and destination, lead times for reservations, and historical price patterns. Analysis of large datasets containing Indian flight data is performed, and algorithms that search for patterns and relationships that impact the cost are studied.