<p>The integration of technology into education, though beneficial, has also raised concerns about academic dishonesty. Digital tools and platforms provide new opportunities for students to engage in behaviors that could undermine academic integrity. Understanding why students commit these acts is crucial for designing effective policies to mitigate these behaviors. This study applies Waltzer and Dahl’s Situated Decision-Making (SDM) model to the context of technology-enabled academic dishonesty. It also employs Machine Learning (ML) techniques to predict students’ engagement in dishonest behaviors based on their perceptions, moral evaluations, and competing motivations. The findings reinforce the SDM model’s core assumptions, showing that these factors are significant predictors of technology-enabled academic dishonesty. Among the formulated and evaluated ML models, the Artificial Neural Network (ANN) exhibited the best predictive performance. To foster a culture of integrity, Higher Education Institutions (HEIs) must clearly define what constitutes academic dishonesty and emphasize its moral unacceptability in technology-integrated classrooms, irrespective of external pressures and competing motivations.</p>