Thermal imaging is a valuable tool in fields like surveillance, medical diagnostics, and industrial monitoring. However, gathering high-quality thermal data can be costly and restricted by environmental factors and hardware limitations. It establishes to create synthetic thermal images with high precision using LSTM networks and Autoencoder algorithms. The LSTM’s ability to recognize temporal dependencies is employed to predict future frames of thermal images, while the Autoencoder framework improves spatial quality and reconstructs images with high accuracy. Through extensive experiments, the effectiveness of this approach is demonstrated, showcasing its ability to generate realistic thermal images with exceptional detail preservation and minimal noise.