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This study employs advanced computational intelligence to address urban carbon reduction through optimized spatial configurations. A Deep Reinforcement Learning (DRL) framework is proposed that integrates Points of Interest (POI), Areas of Interest (AOI), and Transportation System Data (TSD) to generate fine-grained carbon emission maps guiding land-use adjustments. In the case study of Hangzhou, China, results show that a carefully designed reward function enables the DRL agent to selectively optimize land-use structures, prioritizing the centralization of residential, dining, and commercial areas to form high-density, mixed-use urban clusters. This spatial reorganization leads to notable reductions in carbon emissions and improvements in resource-use efficiency. The proposed DRL-based framework provides a scientific basis for policy development toward sustainable land-use and urban density optimization. By merging advanced AI techniques with urban planning, this research contributes to the creation of low-carbon, resilient, and environmentally sustainable cities capable of addressing global climate challenges. The optimized DRL agent achieved carbon emission reductions of up to 15% compared to baseline configurations in the Hangzhou case study. Spatial concentration analysis revealed a 23% increase in residential area clustering and 31% increase in commercial zone centralization over 400 training episodes. The PPO-based model demonstrated superior performance compared to genetic algorithm and linear regression baselines, with lower policy loss (converging to \u003C0.01) and critic loss (converging to \u003C0.005) after early stopping at 400 episodes. 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