The steady advancement of online education has not automatically translated into improved educational quality. Teacher training often continues to focus on the technical use of digital tools, while the pedagogical processes through which teachers report supporting students’ digital competence remain insufficiently understood. The objective of this study was to examine the sequential and predictive structure of teachers’ digital competence using the DigCompEdu framework as a reference. A quantitative cross-sectional study was conducted with a sample of 136 university teachers involved in online education. Data were collected through a self-reported questionnaire based on DigCompEdu and analyzed in two phases: Partial Least Squares Structural Equation Modeling (PLS-SEM) and Artificial Neural Networks (ANNs). The PLS-SEM results suggested a sequential pattern of associations among teacher-reported constructs: Professional Commitment (PC) was positively associated with Digital Resource Management (DR), which in turn was positively associated with Digital Pedagogy (DP) and Assessment and Feedback (AF). These dimensions were associated with Student Empowerment (SE), which showed the strongest positive relationship with teachers’ reported practices for Facilitating Students’ Digital Competence (FS). The ANN sensitivity analysis showed adequate predictive performance in the testing phase (RMSE = 0.155) and identified Student Empowerment as the predictor with the highest normalized importance within the specified model. These findings suggest that faculty development in online higher education may benefit from moving beyond basic digital literacy and platform management toward pedagogical design, formative assessment, inclusive participation, and learner agency. However, the results should be interpreted as evidence of teacher-reported facilitation practices within the analyzed sample, rather than as direct evidence of students’ actual digital competence development.