General take-up of electric vehicles (EVs) is essential to support sustainable transport, but their climate effectiveness depends on how the EV is charged. Incentivization of current conventional charging is irrelevant to the indirect impacts of fossil fuel-based electricity use, and also no pricing reflects linked carbon effects. This chapter puts forward an artificial intelligence-based low-carbon EV charging scheduling paradigm that aims at optimization by utilizing instant information of the price of electricity, the grid’s carbon content, and availability of green energy. With the integration of predictive modeling, machine learning, and reinforcement learning, the system makes dynamic adjustments in charging to mitigate emissions and expenses while alleviating grid load. A carbon emission flow model allows real-time monitoring of per-session emissions. The framework also supports peer-to-peer (P2P) green energy trading through blockchain smart contracts, permitting EV owners to buy renewable energy directly. Dynamic pricing strategies further coordinate charging with low-carbon grid hours, enhancing grid resilience and sustainability. Real-world simulations prove significant emissions and peak load reductions, and carbon trading-generated financial incentives. This AI-enabled system is a scalable carbon-aware EV charging solution despite its challenges through computational complexity and regulatory barriers. It helps in achieving net-zero goals by establishing a smart, efficient, and sustainable EV energy infrastructure.