Protein fold prediction is important in molecular biology for understanding protein function and designing therapeutics. Traditional methods have led to new approaches by attempting to unravel the complexity of the folded landscape. Our hybrid quantum classical approach combines a different quantum eigensolver (VQE) with deep learning to encode interactions in quantum circuits for efficient use. Deep neutral networks have been observed to obtain information related to energy surfaces as they improve the forecasting accuracy, whereas many proteins offer better predictions with both energy-driven as well as traditional quantum mechanics methods. Folding can be visualized using our model. In the time to come, quantum circuits should be optimized, deep learning models integrated, and biological data integrated with an aim of enhancing predictions. This project shows the possibility for molecular research translation as well as drug development. In conclusion, our combination of quantum computing classical computation approach enhances protein folding precision which is poised to transform molecular biology studies as well as other areas such as drug development among others. More work is still needed to explore this route further before all its secrets can be unveiled concerning protein folding and function.