Software fault prediction (SFP) techniques are used to identify faults at the early stages of the software development life cycle (SDLC). We find machine learning techniques as commonly used techniques for SFP as compared to deep learning methods which can produce more accurate results. Deep learning offers exceptional results in a variety of domains such as computer vision, natural language processing, speech recognition, etc. In this study, we use three deep learning methods, namely, Long Short Term Memory (LSTM), Bidirectional LSTM (BILSTM), and Radial Basis Function Network (RBFN) to predict software faults and compare our results with existing models to show how our results are more accurate. In our study, we use Chidamber and Kemerer (C&K) metrics-based datasets to conduct experiments and test our proposed algorithm. We conclude that LSTM and BILSTM perform better whereas RBFN is faster in producing the required results. We use k-fold cross validation to do the model evaluation. Our proposed models provide a more accurate and efficient SFP mechanism to software developers.