Public interest in the air transport by aircraft occurs year by year is increasing, so this opportunity can be exploited by travel agents to improve transactions and corporate profits. The increase is proportional to the number of transactions from the sale of flight tickets to conventionally processed by travel agents and is not used anymore. Airline ticket sales history data from various airlines and destinations stored over the years can be described, identified factors that affect profitability, and make predictions. This paper provides the processing of the visualization of transaction data and prediction model using regression methods on flight ticket sales on travel agents. The real data are used to predict the profit with predictive analytics. The regression methods that used linear regression, multilayer perceptron (MLP), and M5 Model (M5P). The Visualization was building into a dashboard to analyze the situation of the data using Power BI. The experiment was using Wakaito Environment for Knowledge Analysis (WEKA) to get the best prediction model. All of the three techniques that build provide the best model for prediction. The model is validated by k-folds cross validation, with the value of k being 10. Then evaluated by its performance with the smallest error on Root Relative Square Error (RRSE). The smallest value of RRSE is 4.63% that generated using MLP. This paper explains about how to estimate profit using the best model from the available data.