With the help of Artificial Intelligence, computers can learn to perform better without explicit programming. High-dimensional data, which frequently contains duplicate, irrelevant, and noisy information, presents a significant problem in machine learning. In order to improve efficiency and accuracy, dimensionality reduction algorithms convert big datasets into lower-dimensional representations. Entity-relationship (ER), data flow (DFD), and context flow (CFD) diagrams in a travel agency system are the main topics of this paper's exploration of the function of data modeling in machine learning applications. Furthermore, it talks about how data analytics—descriptive, predictive, and decision analytics—can improve the operations of travel agencies. To guarantee data security, access control techniques like Role-Based Access Control (RBAC) and Database Access Control (DBAC) are investigated. The study also emphasizes the use of machine learning and statistical models in commercial decision-making, such as Euclidean distance and linear regression. When combined, these approaches help to improve service efficiency and optimize travel management systems.