Parkinson’s disease is a progressive neurological disorder that significantly impairs motor functions, particularly gait . Early detection is essential for timely medical intervention and improving patient outcomes. In this paper, we introduce TrPrNet, a novel architecture for the early detection of Parkinson’s Disease that leverages a Transformer-based architecture. While previous studies have demonstrated the effectiveness of CNN and RNN-based models, they often fall short in capturing temporal dependencies within sequential data. TrPrNet addresses this limitation by utilizing self-attention mechanisms to understand complex relationships in time-sequenced body gait features, effectively capturing both short-term and long-term interactions. We evaluate TrPrNet against other RNN-based deep learning models such as LSTM and GRU, as well as various existing deep learning and machine learning approaches from previous researches. Using body keypoint based gait features extracted from gait sequences as input, our models are trained and tested on a meticulously curated dataset of gait videos. TrPrNet achieves performance, attaining 99.38% accuracy and a loss of 0.0001. These results underscore the potential of our Transformer-based architecture as a highly accurate, non-invasive tool for the early diagnosis of Parkinson’s Disease.