The transition toward carbon-neutral chemical manufacturing requires catalytic systems that can convert abundant waste molecules into value-added chemicals under mild, renewable-energy-driven conditions. Single-atom electrocatalysts (SAECs), featuring isolated metal centers anchored on tailored supports, offer maximum atom utilization, tunable coordination environments, and well-defined active sites for complex multielectron reactions. This review critically examines the emerging role of artificial intelligence in accelerating the discovery, optimization, and mechanistic understanding of SAECs for carbon dioxide electroreduction and green ammonia synthesis through nitrate conversion. First, fundamental design principles are discussed, including metal–support interactions, coordination engineering, electronic-structure modulation, and stability limitations. Next, machine learning, density functional theory integration, high-throughput screening, explainable descriptors, and self-driving laboratory concepts are evaluated as tools for rational catalyst development. Particular emphasis is placed on CO₂ valorization pathways toward CO, formate, hydrocarbons, and alcohols, together with nitrate-to-ammonia conversion as a sustainable nitrogen-recycling strategy. Advanced in situ/operando characterization, performance benchmarking, selectivity control, and degradation mechanisms are also assessed. Finally, this review highlights current barriers related to data quality, catalyst durability, reactor design, product separation, techno-economic feasibility, and industrial scale-up. The article provides a forward-looking framework for integrating AI, atomically precise catalysis, and renewable electrosynthesis in future sustainable chemical manufacturing.