This paper presents a machine learning-based model designed to forecast trends in enrollment in the Department of Computer Studies at NEMSU–Cantilan Campus, specifically for the Bachelor of Science in Computer Science (BSCS), Bachelor of Science in Computer Engineering (BSCpE), and Bachelor of Science in Information Technology (BSIT) programs. Twenty semesters (2015–2025) of historical program-level enrollment were chronologically split, with the most recent semesters withheld for validation, and a lagged feature set (Lag 1–Lag 3) was constructed for an Artificial Neural Network (ANN) and benchmarked against Holt-Winters Exponential Smoothing and ARIMA, both tuned on the same training split. Employing the ANN model, this study achieved an aggregate Mean Absolute Percentage Error (MAPE) of 3.62%, outperforming the Holt-Winters (25.59%) and ARIMA (31.09%) baselines on this dataset, a ranking corroborated by RMSE and MAE (Table 6) and confirmed against a small LSTM and Prophet, neither of which outperformed the ANN at this sample size; a per-program breakdown (Table 7), however, shows this advantage is not uniform, ranging from 3.93% MAPE for BSCS to 13.48% for BSIT. The enrollment projections indicate consistent growth across all programs. The BSCS program is likely to increase from 297 students in 2025 to 401 by 2028, exhibiting a 35% growth. The BSIT program is projected to experience the most significant expansion, growing from 1,078 students in 2025 to 2,714 students by 2028-an increase of 151%, a trend consistent with the program’s sharp historical acceleration after 2022 and the recursive nature of the multi-step forecast, which compounds this recent growth forward. Meanwhile, the BSCpE program is expected to grow more gradually, from 153 students in 2025 to 186 by 2028, showing a 21.6% increase. Overall, the total enrollment in the Department of Computer Studies is expected to rise from 1,517 students in the second semester of 2025 to 3,031 by the first semester of 2028, marking a 100% increase. These findings highlight the accuracy and adaptability of ANN-based models in capturing nonlinear trends in enrollment, offering a valuable tool for strategic planning, faculty and classroom allocation in state universities and colleges in the Philippines.