The aviation sector is dynamic and always evolving, and the fluctuating cost of tickets makes it difficult for travelers to plan their trips affordably and plan a trip economically. The differences in air ticket prices exist so much so that always it has been observed that various travelers travel in the same flight with varied ticket prices. This leads to a situation of underbooked flights and high consumer dissatisfaction which ultimately have a negative financial impact on the airline business. Timely flight price forecasting would aid airlines in planning their operations and assembling the resources essential to impact a particular consumer section level for a specific route. With a motive to find the model with the lowest mean absolute error when forecasting the costs of a journey, this work tries to offer a model that effectively incorporates the variation among many elements determining the price of an airfare. Prices for several airlines’ flight tickets were provided for the dataset in this study, which was obtained from Kaggle. The dataset was engineered efficiently using various feature engineering and selection methods. Afterward, 80:20 data was divided into training and test data, respectively. The model was trained using the Random Regressor Algorithm. The model obtained a Normalized Root Mean Square Error of 0.06. Further, an unseen dataset was fed into the model to predict the flight ticket prices.