Efficient AI Models for Extreme Edge Environments: A Comprehensive Review for Space, Underwater, and Disaster Zones

Harsh edge environments like outer space, deep oceanic areas, and disaster zones pose unprecedented challenges to the deployment of Artificial Intelligence (AI) systems. Such environments are marked by stringent limitations in terms of computational resources, communication bandwidth, energy availability, and operational resilience. Conventional AI models, being commonly resource-hungry, are poorly suited for such environments. To fill this gap, lightweight AI models - designed for efficiency, compactness, and robustness - have become a key technology enabler. This review presents an exhaustive overview of the latest developments in lightweight AI specifically designed for extreme edge conditions. We examine the particular requirements created by space missions, underwater environments, and disaster response situations. Major modeloptimization techniques such as pruning, quantization, neural architecture search, and transfer learning are explained in great detail. Application-based case studies in space rovers, underwater autonomous vehicles, and disaster rescue drones illustrate the revolutionary effect of light AI. In addition, the paper discusses long-standing issues including model robustness, data availability, and hardware constraints, and outlines directions for future research focusing on explainability, self-healing systems, and the intersection of AI with promising technologies such as blockchain and federated learning. Through the integration of findings across environmental domains, this review hopes to inform the creation of next-generation AI solutions that can perform reliably in the most extreme conditions.