In the dynamic and highly competitive airline industry, accurate flight price prediction is paramount for both travellers seeking cost-effective options and airlines optimizing revenue management. This study presents a comprehensive analysis of three distinct machine learning algorithms – Decision Trees, Random Forest, and Logistic Regression – in the context of flight price prediction. Firstly, Decision Trees, known for their simplicity and interpretability, are examined. We explore their capacity to capture pricing patterns and their limitations in handling complex relationships within the dataset. Next, Random Forest, an ensemble method, is investigated to assess its ability to improve prediction accuracy by combining multiple decision trees. Finally, Logistic Regression, a widely-used classification algorithm, is adapted for flight price prediction, highlighting its strengths in handling binary outcomes and potential benefits when applied to ticket pricing. The study utilizes real-world flight pricing data and evaluates the performance of each algorithm in terms of r2 score , and mean squared Error mean absolute Error  . Additionally, feature importance analysis is conducted to uncover the key factors influencing flight prices. By comparing the three machine learning approaches, this research aims to provide valuable insights into the strengths and weaknesses of each method in the context of flight price prediction. These findings will help stakeholders, including travellers and airlines, make more informed decisions and enhance pricing strategies in the aviation industry. 

 Keywords: Decision Tree, Random Forest, Logistic Regression. LASSO, MLP