The increasing sophistication of evolving malware types and attack techniques has rendered traditional antivirus solutions inadequate, particularly in mitigating zero-day threats. To address this challenge, Machine Learning (ML) and Deep Learning (DL)-based approaches have been developed, demonstrating significant efficacy and high accuracy in malware classification. However, the black box nature of these models raises significant concerns in terms of transparency and interpretability. This study presents a comprehensive evaluation of Ensemble Learning and Deep Learning methods for static analysis-based malware classification, which allows joint analysis of Application Programming Interface (API) calls and Dynamic Link Library (DLL) data. In the study, a specially designed Convolutional Neural Network (CNN)-Gated Recurrent Units (GRU)-3 model is trained using a tailored dataset consisting of malicious and secure software. In order to better understand the model’s performance, feature importance analysis was performed using SHapley additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) Explainable Artificial Intelligence (XAI) techniques and the reliability of model decisions was increased. The proposed model was compared with DL models such as CNN, Long Short-Term Memory (LSTM), and GRU, as well as traditional ML algorithms such as Extreme Gradient Boosting (XGB), Extra Trees Classifier (ETC), K-Nearest Neighbor (KNN), and Random Forest (RF). While the traditional XGB model achieved the highest overall performance with a 99.81% accuracy rate, our proposed CNN-GRU-3 model demonstrated the best performance among all DL models, reaching a 99.37% accuracy rate. This study presents a powerful framework that provides both high accuracy in malware detection and makes the decision mechanism more transparent.