Advancements in software development within the era of Industry 4.0 are prompting a reevaluation of traditional risk management methodologies. This survey investigates the integration of machine learning (ML) with established frameworks like those from the Project Management Institute (PMI) and ISO 31000. The study focuses on the potential of ML to enhance risk evaluation and mitigation in the Software Development Life Cycle (SDLC), while also addressing the challenges it brings, such as data privacy concerns and the risk of biased algorithms, especially in dynamic and regulated environments. This research underscores a significant gap in the literature, highlighting the lack of studies specifically focusing on risk assessment in software production using ML, thus positioning itself as a meta-research study. The findings point to a paradigm shift toward an interdisciplinary approach that merges ML with traditional risk management techniques. Despite the complexities and ethical dilemmas introduced by ML, the study emphasizes the dual role of ML in enhancing software quality and introducing intricate challenges, highlighting the need for continuous research and innovation for the effective integration of ML in software risk management.