<p>Parkinson’s disease (PD) is a progressive neurological disorder that affects both motor and cognitive domains. While recent studies have investigated EEG-based classification, few have addressed the continuous estimation of clinical scores using interpretable models. In this study, we propose a novel deep fuzzy rule-based model designed to predict two key clinical indices—Montreal Cognitive Assessment (MoCA) and Unified Parkinson’s Disease Rating Scale (UPDRS)—directly from task-based EEG signals recorded during cognitively demanding interval timing tasks (3s and 7s). Our model combines nonlinear entropy and recurrence-based features extracted from the multiple phases—where cognitive load is maximal—with a layered fuzzy rule structure guided by Fuzzy clustering, enabling interpretable reasoning. The model significantly outperforms conventional regressors, fuzzy models (ANFIS, and GMM-based deep fuzzy types) in all evaluation metrics. It achieves the lowest MAE (2.05 ± 0.41), highest R² (0.107 ± 0.081), and highest Spearman’s ρ (0.270 ± 0.148) for MoCA estimation in the 3s task, and shows similar superiority in the 7s task (MAE = 2.18 ± 0.43, R² = 0.135 ± 0.104, ρ = 0.269 ± 0.129). For UPDRS-III estimation, it also achieves leading results (short task: MAE = 5.31 ± 1.28, R² = 0.045 ± 0.068, ρ = 0.137 ± 0.135; long task: MAE = 5.24 ± 1.38, R² = 0.064 ± 0.074, ρ = 0.163 ± 0.119). These findings highlight the clinical potential of interpretable deep fuzzy systems for dual-score estimation in PD, providing a transparent, and physiologically grounded framework for assessing disease severity from cognitive-task EEG complexity.</p>