Objectives: Accurate severity classification is important for sensor-based assessment of Parkinson’s disease, but overlap between adjacent stages and class imbalance can reduce model robustness. This study aimed to develop and evaluate a dual-task gait fusion framework that integrates signals collected during self-selected walking and walking with turning. Methods: The primary cohort comprised 87 participants with Parkinson’s disease across mild, mild-to-moderate, and moderate stages. Task-specific temporal representations were learned from the two gait conditions, concatenated and optimized using delayed class re-weighting. Performance was evaluated using subject-level stratified five-fold cross-validation with five random seeds. Generalizability was further assessed using an independent PhysioNet cohort of 93 participants with Parkinson’s disease. Results: The proposed method achieved an accuracy of 90.23 ± 1.27, balanced accuracy of 89.61 ± 1.13, macro F1-score of 89.10 ± 1.35, and probability-based macro AUC of 94.43 ± 1.42 on WearGait-PD. Confusion matrix and ablation analyses indicated balanced class-level performance and complementary contributions from dual-task fusion and delayed re-weighting. On the external PhysioNet cohort, accuracy, balanced accuracy, macro F1-score, and macro AUC were 84.34 ± 1.82, 82.13 ± 1.92, 81.68 ± 1.98, and 88.93 ± 2.14, respectively. Conclusions: Integrating turning-related gait information with self-selected walking signals improved wearable sensor-based severity classification and showed cross-dataset robustness, although validation in larger cohorts with complete severity stage coverage remains necessary.