Mangroves represent highly productive coastal ecosystems that play a pivotal role in maintaining biodiversity, shoreline stabilization, carbon sequestration, and providing ecosystem services critical to climate change adaptation. Despite their importance, mangroves worldwide are experiencing rapid degradation due to anthropogenic activities, climate-related disturbances, and environmental stressors. Therefore, accurate and timely monitoring of MEs is essential for conservation and sustainable management. Remote sensing (RS) technologies have emerged as indispensable tools, offering extensive coverage and repeatability across inaccessible coastal landscapes. This chapter presents a comprehensive overview of various RS sensors—such as optical, synthetic aperture radar (SAR), light detection and ranging (LiDAR), hyperspectral, thermal infrared, and UAV-based platforms—and discusses their unique strengths, limitations, and suitability for monitoring MEs. Specific attention is given to sensor-derived parameters like vegetation indices, canopy structure, biomass estimation, hydrological assessment, species discrimination, and stress detection. To contextualize the practical application of these technologies, this chapter features the Sundarbans mangrove ecosystem as a representative study area. Sundarbans, the world’s largest contiguous mangrove forest, faces constant threats from sea-level rise, cyclones, and anthropogenic pressures, making it an ideal example to illustrate remote sensing’s operational effectiveness. The integration of multi-sensor and multi-platform RS approaches, supported by advancements in artificial intelligence and machine learning, is highlighted as the future direction for enhancing monitoring precision and ecosystem resilience. Ultimately, this synthesis not only provides critical insights into current RS capabilities but also emphasizes the importance of adopting integrative technologies for improved decision-making in mangrove conservation globally.