Windows-based malware continues to evolve in complexity, leveraging obfuscation, polymorphism, and anti-analysis techniques that bypass traditional security systems. The rapid evolution of malware targeting Windows systems poses significant challenges to conventional detection techniques, which struggle to adapt to dynamic and obfuscated threats. This study presents a novel neural network framework that integrates static and dynamic analysis through an Adaptive Feature Integration Module, hybrid convolutional-recurrent layers, and an ensemble decision mechanism. The proposed model employs a progressive, multi-stage training strategy on diverse datasets, including EMBER, EMBERSim, and SoReL-20M, to enhance generalization and resilience against emerging malware variants. Experimental results demonstrate that our approach achieves superior accuracy, precision, recall, and F1 scores compared with related methods. It achieved an average detection accuracy of over 96% across the evaluated datasets. The framework effectively captures local and temporal patterns inherent in malware behavior, mitigates overfitting, and adapts to new data without catastrophic forgetting. This innovative integration of advanced deep learning techniques represents a substantial advancement in Windows malware detection, offering improved performance and robustness for real-world cybersecurity applications.