In the light of dynamic pricing policy, flying is becoming too expensive, and it’s really hard to book tickets at proper prices. In response to this, researchers began discussions on how machine learning models could be used to predict an approximate fare for a flight so that the passenger could purchase at the most ideal time to get low fares. These models consider travel dates, destination, airlines, stopovers, timing of booking, holidays and demand. Techniques used are Decision Trees, Random Forest, Gradient Boosting and ANNs. These have different strengths-some, such as ensemble methods, Random Forest and Gradient Boosting, with high robustness in terms of predictions because they average multiple decision paths; ANNs represent complex, non-linear relationships but at the cost of significant computation. Model performance was evaluated using Mean Absolute Error, Root Mean Square Error and R-squared. This research informs passengers on price trends, and better booking decisions will be achieved. Real-time data integration and more advanced algorithms comprise future improvement prospects. The research work bridges the gap between revenue strategies employed by airlines and the need of the travellers to travel affordably, thereby optimizing passengers travel costs.