<p>Carbon emissions drive climate change, and carbon credits mitigate climate deterioration and environmental damage while assisting organizations in managing their carbon footprint. Fully utilizing carbon credits remains challenging. This study enhances understanding of the engineering practices for carbon credits to develop responsible fintech solutions and provide insights for carbon emission management. We review the negative impacts of organizations’ strategy of evading carbon management through non-disclosure of carbon emissions. Evidence shows that both non-disclosure of carbon emissions and high carbon emissions negatively affect an organization’s financial stability and market value, suggesting that organizations should manage carbon emissions and transparently share information to mitigate risks. We examine engineering methods for more cost-effective carbon management, focusing on data-driven computing solutions: factors influencing carbon prices, carbon price prediction algorithms for optimized carbon credit purchasing strategies, and corporate carbon emission prediction algorithms. These methods enable performance assessments for investors and governments when carbon emissions data is not disclosed and help organizations estimate future carbon credits needs for budget planning and strategy optimization. Finally, integrating carbon price and carbon emission predictions, we propose future research directions, including prediction of corporate-level carbon management costs, laying a foundation for quantitative research on how carbon management practices impact corporate market value and financial performance. This systematic review provides a comprehensive synthesis of carbon credits with a unique focus on computing solutions and engineering practices, highlighting AI’s role in enhancing transparency and fostering social accountability for an inclusive and trustworthy low-carbon transition.</p>