In the rapidly evolving domain of software engineering, the imperative to deliver high-quality software products has never been more pressing. Traditional software quality assurance practices, which primarily rely on manual code reviews, extensive testing, and debugging processes, often encounter significant limitations. Conventional methodologies such as Waterfall and Agile, while structured, frequently struggle to predict and prevent defects in the early stages of the software development lifecycle. This inability to identify issues proactively contributes to increased costs, delayed project timelines, and diminished software reliability. The escalating complexity of modern software systems, combined with tight project schedules and the ever-increasing demand for high-quality products, underscores the necessity for innovative approaches to software quality prediction. In this context, machine learning (ML) methods emerge as a promising solution, enabling teams to leverage historical data to uncover patterns and correlations that may not be apparent through traditional methods. By analyzing past project data, ML algorithms can identify key indicators of software quality and predict potential defects before they materialize, thus facilitating timely interventions.