In the realm of cybersecurity, the detection of malware in executable files represents a pressing challenge. Conventional signature-based methods often struggle to keep pace with evolving threats, necessitating innovative solutions. This research investigates the application of advanced machine learning techniques, specifically Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) architectures, augmented by word embedding methodologies, for robust malware detection. The research initiates with a systematic investigation of fundamental machine learning principles and rigorous data processing methodologies, forming a robust foundation for subsequent phases. Leveraging this acquired knowledge, the study embarks on the creation and refinement of a specialized deep learning model intricately designed for the accurate detection of concealed malware within executable files.