PDF malware refers to malicious software or code that is embedded within PDF (Portable Document Format) files, which are commonly used for sharing and distributing information. In our proposed system for detecting PDF malware, we integrate Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) networks to enhance our detection capabilities. RNNs and LSTMs are adept at capturing temporal dependencies, making them ideal for identifying evolving patterns in PDF malware. By leveraging these networks, our system learns to recognize subtle changes in PDF structures indicative of malicious content. This integration improves predictive accuracy while streamlining the training process, thanks to the recurrent nature of RNNs and LSTMs, which enable effective learning from past steps. The combination of pre-trained models and advanced neural network architectures significantly reduces training times without compromising detection precision. Overall, our hybrid approach represents a powerful advancement in cybersecurity, providing a more adaptable, accurate, and efficient defense against dynamic PDF-based malware threats.