The relationship between stress, mental workload (MWL), and eye movement patterns is well-established but remains complex and not fully understood. Recently, artificial intelligence (AI) has shown considerable potential across various fields, emerging as a powerful tool for analyzing complex relationships. This thesis builds on previous research that used AI to assign a single class per task in postprocessing based on six biological signals. Here, the objective is to achieve real-time classification using only eye movement data, assigning a stress or MWL class every few seconds. Conducted at Politecnico di Torino, this study involved 103 participants in an experimental design aimed at inducing stress and MWL through two cognitive tasks: the Stroop test to induce stress and the N-Back test to increase MWL, with rest phases interspersed.