This chapter looks at the creative application of remote sensing (RS) and artificial intelligence (AI) technologies in aquaculture to enhance productivity, optimization, sustainability, efficiency and environmental management. RS technologies, capable of monitoring large and inaccessible areas, are paired with advanced machine learning (ML) algorithms developed to process complex datasets and execute intelligent tasks like examining cause-effect associations. RS has changed our view of the Earth’s surface, including air, water, and land. It offers precise, real-time data crucial for assessing mapping, temperature, water quality, biomass, and monitoring marine oil spills and cages. ML models, including supervised, unsupervised and reinforcement learning techniques, are employed to monitor species, identify diseases and algal blooms, analyses oceanographic data, optimize feeding schedules, analyses fish market trends, predict prices, and examine socioeconomic factors. Moreover, integrating RS and subdivision of AI involves collecting ecological data, processing, and real-time decision-making to intensify aquaculture production. Case studies demonstrating successful applications provide a practical perspective. This chapter concludes by assessing these technologies’ current challenges and future potential in transforming aquaculture practices. Implications for on-the-ground operations, policy, and sustainability metrics are discussed to underline the transformative impact of merging these technologies.