As the world supply chains become more sophisticated, a sense of urgency in a manner of approach has been introduced to address the requirements of profitability like the environmental and social responsibility criteria. The classical models of supply chain management (SCM) paradigm could barely balance on efficiency, sustainability and resiliency at least as concerns the variable market conditions and the ever increasing high stake-holders demands. The provided paper explains how predictive analytics, optimization algorithms, and decision-support systems can be applied in terms of sustainable supply chain management with the help of machine learning (ML). The ML strategies enable the business enterprises to generate the demand more effectively, reduce the wastes and improve the logistics besides monitoring the carbon footprint, labor norms and responsible sourcing. Evidence Case-based evidence has demonstrated that, (i) ML applications, which involve supplier risk anomaly detection, logistics optimization due to reinforcement learning and sustainability reporting due to natural language processing could be implemented to create profitability and corporate responsibility. The integration of business performance and sustainability objectives creates an opportunity of making business strategic source of competitive advantage and level of social contribution of business provided by ML. The results are an addition to the fact that the digital intelligence and responsible governance assimilation is where responsible supply chains are, and, therefore, the companies would be able to remain profitable and responsible in response to the challenges that the world imposes on them.