Engineering education is increasingly incorporating smart mechanical technologies such as robotics, sensor-integrated systems, and IoT-enabled tools in response to Industry 4.0 demands. While the technical benefits are clear, limited research explores the behavioral factors influencing students’ intention to adopt these technologies. This study investigates the predictors of intention to use smart mechanical technologies by integrating the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) with additional constructs such as Hedonic Motivation, Personal Innovativeness, and Job Threats. A dual-stage analytical approach was adopted, combining Partial Least Squares Structural Equation Modeling (PLS-SEM) and Artificial Neural Network (ANN) analysis. PLS-SEM results indicate that Performance Expectancy, Effort Expectancy, Social Influence, and Hedonic Motivation significantly influence students’ behavioral intention, while Facilitating Conditions and Job Threats were not supported. ANN analysis confirmed the predictive relevance of the model and identified Hedonic Motivation and Effort Expectancy as the most influential predictors across models. The integration of linear and non-linear techniques offers a more comprehensive understanding of adoption behavior, emphasizing the importance of these elements in preparing students for future technological environments.