As climate change accelerates, there is an urgent need for solutions that balance ecological responsibility with economic incentives. While capping carbon emissions is widely recognized as essential, it remains a challenging task to quantify carbon sequestration correctly and ensure complete transparency in carbon credit markets. The increasing demand for effective carbon sequestration measurement and transparent carbon credit trading demands an innovative approach using advanced technologies. This research focuses on applying big data using Kafka for parallel data streaming in a distributed environment, together with machine learning models to optimize the prediction of carbon capture, integrating blockchain technology which provides security and transparency in transactions involving the carbon credit market. Through our research, we aim to provide an interdisciplinary framework that will improve the accuracy and scalability of carbon sequestration predictions, building trust and accountability in carbon trading to support a more sustainable and economically viable future.