This study presents an experimental and comprehensive data-driven framework to gain deeper insights into the effect of acidic pretreatment in enhancing the biomethane yield of Xyris capensis. The experimental workflow involves subjecting the Xyris capensis to different concentrations of HCl, exposure times, and digestion retention time in mesophilic anaerobic conditions. Key insights were gained from the experimental dataset through correlation mapping, feature importance assessment (FIA) using the Gini importance (GI) metric of the decision tree regressor, dimensionality reduction using Principal Component Analysis (PCA), and operational cluster analysis using k-means clustering. Furthermore, different clustering techniques were tested with an Adaptive Neuro-Fuzzy Inference System (ANFIS) tuned with particle swarm optimization (ANFIS-PSO) for biomethane yield prediction. The experimental results showed that HCl pretreatment increased the biomethane yield by 62–150% compared to the untreated substrate. The correlation analysis and FIA further revealed exposure time and acid concentration as the dominant variables driving biomethane production, with GI values of 0.5788 and 0.3771, respectively. The PCA reduced the complexity of the digestion parameters by capturing over 80% of the variance in the principal components. Three distinct operational clusters, which are influenced by the pretreatment condition and digestion set-up, were identified by the k-means cluster analysis. In testing, a Gaussian-based Grid-Partitioning (GP)-clustered ANFIS-PSO model outperformed others with RMSE, MAE, and MAPE values of 5.3783, 3.1584, and 10.126, respectively. This study provides a robust framework of experimental and computational data-driven methods for optimizing the biomethane production, thus contributing significantly to sustainable and eco-friendly energy alternatives.