This study introduces a novel hybrid approach for malware detection that combines long short-term memory (LSTM) and convolutional neural networks (CNN). The method uses a malware classification technique that integrates image processing and machine learning. Malware binaries are converted into grayscale images and analyzed using CNN-LSTM networks. Dynamic features are extracted, ranked, and reduced via Principal Component Analysis (PCA). Various classifiers are employed, with the final classification achieved through a voting scheme. The approach processes binary code inputs, where LSTM captures temporal dependencies and CNN performs parallel feature extraction. PCA is used for feature selection, reducing computational time. The proposed method was evaluated on a public malware dataset and demonstrated state-of-the-art performance in identifying various malware families. It significantly reduces the resources required for manual analysis and improves system security. The approach achieved high precision, recall, accuracy, and F1 score, outperforming existing methods. Future research directions include improving feature extraction techniques and developing real-time detection models.This study introduces a novel hybrid approach for malware detection that combines long short-term memory (LSTM) and convolutional neural networks (CNN). The method uses a malware classification technique that integrates image processing and machine learning. Malware binaries are converted into grayscale images and analyzed using CNN-LSTM networks. Dynamic features are extracted, ranked, and reduced via Principal Component Analysis (PCA). Various classifiers are employed, with the final classification achieved through a voting scheme. The approach processes binary code inputs, where LSTM captures temporal dependencies and CNN performs parallel feature extraction. PCA is used for feature selection, reducing computational time. The proposed method was evaluated on a public malware dataset and demonstrated state-of-the-art performance in identifying various malware families. It significantly reduces the resources required for manual analysis and improves system security. The approach achieved high precision, recall, accuracy, and F1 score, outperforming existing methods. Future research directions include improving feature extraction techniques and developing real-time detection models.