This study addresses the problem of inadequate guidance for learners in Competency-Based Education (CBE) when selecting career pathways, as current models often overlook important factors such as academic performance, personal interests, extracurricular activities, and career goals. In CBE, learners follow personalized, flexible learning paths based on their prior knowledge and skills, but career pathway decisions are frequently influenced by parents, teachers, and career counselors, missing the critical elements that help learners make informed choices. While recommender models are widely used in education for course selection and career advising, they have typically failed to integrate these diverse factors comprehensively. To address this gap, the study developed a hybrid recommender model designed to enhance career pathway selection in CBE. Using a mixed-method research design, data was collected through an online survey of 1,487 teachers from junior secondary schools in Meru County, focusing on factors influencing career pathway decisions. Analysis done in SPSS revealed that academic performance, personal interests, extracurricular activities, career goals, and job market trends are crucial to these decisions. Based on these insights, a CBE senior school dataset was created, and a hybrid recommender model was developed using hybrid filtering, deep neural networks, and random forest algorithms, combined through a stacking ensemble method. The model was validated using k-fold cross-validation and achieved an accuracy of 90.06% when applied to STEM career pathway tracks. These findings suggest that the hybrid model is effective in guiding learners toward appropriate STEM career pathway tracks in CBE. Future work could explore more advanced algorithms and expand the model to include additional career pathways.