With the growing emphasis on data-driven decision making, artificial intelligence (AI) methods have become increasingly important in managerial practice. This study aims to develop and evaluate supervised machine learning models for predicting customer brand loyalty and satisfaction based on selected behavioral, attitudinal, and programmatic attributes. This paper presents a lightweight decision support application that leverages machine learning techniques—specifically, Artificial Neural Networks (ANN) and Support Vector Machines (SVM)—to predict key customer-related indicators: brand loyalty and satisfaction. The models were trained on behavioral and attitudinal inputs and achieved excellent predictive performance, with test accuracies reaching 100%. The novelty of this study lies in the deployment of these models within an intuitive graphical user interface (GUI), enabling real-time predictions by non-technical users. Unlike traditional approaches focused solely on algorithm development, this research demonstrates a practical implementation of computational intelligence for operational and tactical business decision-making. The tool supports managers in profiling customers, optimizing loyalty programs, and enhancing customer engagement strategies through accessible AI-powered insights.