Side-channel attacks (SCAs) exploit power analysis to extract secret information. Researchers have employed this technique to disassemble software and retrieve cryptographic keys by examining power consumption or electromagnetic emissions. They utilized hardware or Hamming-based fluctuations measurement to profile or model the power leakage. Developers employ power modeling to comprehend software leakage, although manually profiling the power trace across various devices and architectures requires time and effort. This work proposes a custom deep learning (DL) method to model the power trace. The DL model was trained to analyze how each assembly instruction produces leakage based on its context with other instructions. The proposed method can predict the power trace with 0.9963 R² from unseen assembly instructions. This method automates device leakage testing and captures contextual and non-linear relationships to help developers understand the software behavior, significantly reducing the time and effort required for power modeling. The potential impact of this DL model on software security is that it can effectively mitigate the risk of SCAs, thus enhancing the overall security of software systems.