Accurate and timely diagnosis of neuromuscular disorders (NMDs), including myopathy and neuropathy, is essential for initiating effective treatment and mitigating disease progression. Despite advances in machine learning for NMD analysis, existing models often rely on datasets with limited subject diversity, focus on binary classification, and lack clinical interpretability, which limits their applicability. Classification remains challenging due to overlapping electromyographic (EMG) signal patterns, high inter-patient variability, and subjective interpretation. To address these challenges, this paper presents an interpretable deep learning framework for multiclass NMD classification using intramuscular EMG signals. A robust feature set is constructed by combining three statistical feature selection methods with clinically validated descriptors. A hybrid Convolutional Neural Network–Long Short–Term Memory (CNNLSTM) model is proposed to capture both spatial patterns and temporal dynamics in EMG data. To ensure transparency and clinical trust, the model integrates explainability techniques, including Permutation Feature Importance, SHapley Additive exPlanations, Partial Dependence Plots, and Local Interpretable Model-Agnostic Explanations, which provide both global relevance and case-specific diagnostic reasoning. Evaluated primarily on the Mendeley dataset and further validated on the EMGLAB and a private non-invasive surface EMG cohort, the framework achieves 95.83% accuracy, 98.61% area under the curve, and strong agreement scores (Cohen’s kappa: 93.75%, Matthews Correlation Coefficient: 93.82%) with a median inference latency of 59.56 ms per sample. These findings validate the framework as a practical and interpretable tool for near real-time NMD diagnosis, enabling clinically actionable decisions in both highdemand and resource-constrained healthcare settings.