Air-ground heterogeneous cooperative sensing systems have emerged as a promising paradigm in multi-agent perception and intelligent decision-making, and they are increasingly deployed in smart cities, disaster relief, and environmental monitoring. Specifically, these systems leverage the complementary strengths of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) to achieve robust perception, precise localization, and coordinated task execution in complex, dynamic environments. Against this backdrop, this paper presents a comprehensive survey of recent advances in air-ground cooperative systems, which is structured around four key areas: multimodal perception fusion, cooperative localization and mapping, path planning, and control strategies. It also highlights representative methods such as distributed state estimation, cross-view semantic feature alignment, and integrated perception–decision–control frameworks. Moreover, this review analyzes typical application cases and identifies major challenges, including heterogeneous data fusion, limited communication bandwidth, and the absence of standardized datasets and evaluation metrics. Looking ahead, the paper discusses emerging trends such as swarm-intelligent coordination mechanisms, unified cross-modal representation models, and lightweight end-to-end architectures. Ultimately, this paper’s goal is to provide theoretical insights and practical guidance for building high-robustness, high-autonomy air-ground cooperative systems suitable for deployment in real-world multi-agent scenarios.