The construction industry is a significant contributor to global waste generation, with residential building construction being a prominent source. In an effort to address the environmental impact of construction waste, this study critically explores the application of machine learning (ML) techniques in source identification, quantification analysis, and prediction models specific to residential building construction waste. The research investigates existing methodologies and identifies gaps in traditional approaches, motivating the integration of ML for enhanced accuracy and efficiency. The study begins by providing a comprehensive overview of current waste management practices in the residential construction sector. Leveraging ML algorithms for source identification, the research evaluates the potential of supervised learning and unsupervised learning models in categorizing and attributing waste to specific sources within the construction process. Moreover, the research delves into the quantification analysis of construction waste, examining the efficacy of ML algorithms in estimating waste volumes based on diverse parameters such as project scale, construction materials, and building design. By examining historical data and incorporating real-time variables, the study aims to develop predictive models that can adapt to changing construction practices and environmental regulations. This aspect is crucial for sustainable waste management and proactive decision-making. Furthermore, the study assesses the challenges and ethical considerations associated with implementing ML in construction waste management, emphasizing the importance of transparency, accountability, and inclusivity in model development and deployment. In conclusion, this research provides a thorough examination of the role of machine learning in transforming the identification, quantification, and prediction of residential building construction waste.