Deep learning-based algorithms for human posture estimation are gaining interest due to the vast applications in different fields, namely in human-computer interfaces, sports analysis, and healthcare. Such algorithms capture detailed movements and postures related to the human body and provide insights into motion tracking, gesture recognition, and rehabilitation therapy. Thermal imaging could be applied to analyze human posture with effects on ergonomics, improvement in sports performance, and healthcare. Using thermal imaging and photographs, this research study assesses human posture, which has revolutionary potential in healthcare, athletic performance, and ergonomics for injury prevention and rehabilitation. This research will weigh the pros and cons of applying thermal imaging to diagnose postural-related disorders and be useful in the future. The study involves convolutional neural networks, video processing, and thermal imaging to track posture across multiple modes. A deep learning model was developed to detect posture from movies, thermal pictures, and human photos. In this work, the model was trained using deep learning architectures that include CNNs and RNNs and visualized to understand its predictions and its robustness to pose, lighting, and thermal signature variations. It also proved that deep learning models can be tailored for various imaging modalities-from human-computer interaction, healthcare, and surveillance. The main disadvantages, however, included scarce data, restricted computing power, and uniqueness of complexity. Future areas to be explored may include multi-modal fusion, 3D posture estimation, or further enhancement of model efficiency for real-time applications.