To ensure the quality, dependability, and optimal functioning of software systems, software defect detection (SDD) is a crucial component of software development. Conventional techniques frequently depend on single-modal data sources, which might restrict the range and efficiency of fault identification. This research investigates the importance of AI-based multimodality, which combines and examines a range of data sources, including source code, design documents, execution logs, and test results. Multimodal frameworks can detect correlations, process complex and diverse data sources, and offer a comprehensive knowledge of software behaviour by utilising artificial intelligence (AI). This capability allows for more comprehensive and precise defect detection at different levels of software development, from design and implementation to testing and deployment. Moreover, AI-based multimodality enables proactive defect prevention techniques, strengthens fault prediction, and advances root cause investigation. In this context, this study demonstrates how multimodal approaches can revolutionise SDD by tackling the drawbacks of unimodal approaches in the software industry. In addition to demonstrating its effectiveness in comparison to conventional methodologies, it analyses the difficulties in implementing AI-based SDD using multimodality. Further, this paper highlights the implications of AI-based multimodality for producing software systems that are dependable, efficient, and of high quality.