Software defect prediction is an important part of software engineering, providing high-quality and trustworthy software systems. Conventional defect prediction models use handcrafted features and traditional machine learning methods, which tend to have poor generalization, low accuracy, and high computational cost. These shortcomings prevent the effective detection of defective software modules, resulting in high maintenance costs and low software reliability. In response to such issues, the present work proposes a Firefly Algorithm (FA)-optimized AI model of Deep Neural Networks for software defect prediction. The DNN model picks up subtle software metrics patterns proficiently, with the addition of FA augmenting the tuning of hyperparameters to bring optimal model efficiency. The experimental assessment illustrates that the suggested DNN-FA model excels compared to conventional methods by reaching an accuracy of 98.5%, computational effectiveness of 97%, parameter sensitivity of 96.8%, convergence rate of 98%, and an error decrease of 95.2%. The prime strengths of the model suggested lie in its high defect detection precision, better computation speed, and good generalizability to a wide range of software projects. Combining the capabilities of nature-inspired optimization and deep learning, the model proves to be cost-effective and high-performing as a solution to predict software defects with minimal need for manual interference and maximizing the reliability of the software. This work's discovery enhances AI-supported software quality checking through a better approach to prediction in contemporary software development systems.