Addressing the global climate crisis necessitates innovative frameworks for effective carbon footprint management. FedCarbonNet integrates artificial intelligence, blockchain, and the Internet of Things to overcome limitations in traditional carbon management systems, including delayed emissions tracking, insecure trading mechanisms, and inadequate predictive analytics. The framework employs IoT sensors for real-time emissions monitoring, federated learning with Recurrent Neural Networks, Long Short-Term Memory networks, and TabTransformer models for accurate forecasting and optimization, and blockchain with smart contracts for secure and transparent carbon credit transactions. Evaluated on a global dataset spanning power generation and aviation, FedCarbonNet achieves a Mean Absolute Error of 0.082 million metric tons (Mt) with a 95% confidence interval of [0.078, 0.086] Mt and an average emissions reduction of 9.96%. Operating across 50 nodes, the system processes 120 transactions per second with an energy consumption of 0.01 kWh per transaction, demonstrating scalability despite challenges with sparse data and computational demands. Future work includes a pilot project targeting a 12% emissions reduction and global deployment. FedCarbonNet provides a robust, scalable solution for real-time carbon management, enhancing accountability and efficiency in alignment with net-zero objectives.