The application of Machine Learning (ML) in software testing aims to automated bug detection and resolution processes. We anticipate the culmination of such developments to result in system autonomy by 2025. Traditional testing approaches have yet to address the ever-growing architectural complexity and scale of software systems, leading them to remain inefficient and riddled with undetected errors. This article aims to shed some light on the intersection between machine learning and software testing, focusing on the automated bug detection, localization, and prediction processes. Key ML methods such as supervised and reinforcement learning and deep learning are explored within the context of testing frameworks. A central proposition of the paper is the detailed framework of machine learning-driven testing systems with the emphasis on the statistical evaluation of various model performance metrics. Further, the paper discusses the limitations and challenges these approaches have yet to tackle at present and in the future. ML systems have the capability to improve various qualitative and quantitative measures of software engineering, particularly within software that undergoes rapid cycles of modification and deployment, also known as continuous integration/continuous deployment (CI/CD) pipelines, as well as systems that require on-the-go error identification. This is evident in the empirical results proving perfect precision, recall and F1 scores across various datasets (0.98; macro avg: 0.98; weighted avg: 0.98).