Biogas production from organic waste is widely recognized as an effective option for renewable energy generation and sustainable waste management. At the same time, accurate estimation of biogas yield remains difficult because the process is influenced by several interrelated factors, including feedstock type, moisture content, and general operating conditions. These factors interact in a nonlinear manner, which limits the applicability of conventional empirical correlations and simplified mathematical models. This limitation is particularly important at the early stages of biogas project development, where quick and reasonably accurate yield estimation is required, while detailed kinetic or operational data are often unavailable. In this study, a machine-learning-based regression model is developed to predict biogas production using experimental data reported in the literature. The dataset is formed from previously published anaerobic digestion studies and includes a range of organic substrates and operating conditions. To ensure practical applicability, only a small number of commonly reported input variables are considered, mainly related to substrate composition and basic process characteristics. This choice reflects the typical level of information available during preliminary design and feasibility assessment. The data are divided into training and testing subsets using a standard train–test split in order to evaluate the predictive capability of the model for unseen data. The performance of the proposed model is evaluated using statistical indicators, with the coefficient of determination (R²) used as the main metric. The results show good agreement between predicted and experimental biogas production values. For the test dataset, the R² value is close to 0.9, indicating that the model is able to explain a large portion of the variability observed in the experimental data. These results suggest that the main relationships affecting biogas generation can be captured using a single regression-based machine learning model, without the need for complex model structures or extensive input data. The obtained results highlight the potential of data-driven regression approaches as practical tools for early-stage analysis of biogas systems. While the proposed model does not replace detailed process modeling or experimental studies, it can support preliminary feasibility analysis, comparison of different feedstocks, and initial technology assessment. Due to its simplicity and modest data requirements, the model may be useful for engineers and researchers involved in the early planning and evaluation of biogas-based renewable energy projects.