<p>The integration of Artificial Intelligence (AI) with satellite remote sensing has fundamentally transformed environmental monitoring from classical physical models to modern deep learning (DL). This review addresses a central question: when, why, and to what extent do AI-driven approaches outperform classical methods in monitoring environmental hazards, and what fundamental limitations persist? We systematically analyze over 200 studies across four critical domains where satellite observations and AI are most effectively combined: wildfires, floods, droughts, and land cover change. Our analysis traces the evolution from index-based and physical models through classical machine learning (ML) to contemporary DL architectures, including convolutional neural networks, recurrent networks (LSTM, ConvLSTM), transformers, and emerging geospatial foundation models. DL demonstrates several important advantages, including automatic feature learning from multispectral imagery, spatial context awareness, temporal modeling of slow-onset and rapid hazards, and integration of heterogeneous satellite data (optical, SAR, LiDAR). However, persistent limitations constrain operational deployment, including data scarcity and domain shift across regions, inadequate uncertainty quantification, physical inconsistency with governing laws, and underutilization of remote sensing data in operational forecasting. Physical boundaries, including chaotic fire behavior, intrinsic predictability limits of meteorological forcing, and spectral saturation in dense forests, impose fundamental constraints regardless of algorithmic sophistication. This review provides a roadmap for next-generation environmental intelligence, identifying hybrid physics-AI modeling, foundation models, and uncertainty-aware architectures as the most promising frontiers. By bridging current capabilities and future potential, the review offers guidance for researchers developing trustworthy and scalable solutions at the intersection of Earth observation and AI.</p> Graphical Abstract <p></p> <p>This figure illustrates the conceptual structure for review of DL in environmental hazard monitoring using satellite remote sensing. Data section shows that there are numerous data sources, including satellite-based Earth observation missions (i.e., Sentinel, Landsat), as well as various sensor modalities employed in environmental applications, such as optical, SAR, thermal, and LiDAR Data. Analysis Stage illustrates methodology employed within the review, which includes: literature review, taxonomy of methods, comparison of approaches. Model layer indicates the differences between classical ML algorithms and the DL architectures, including Convolutional Neural Networks, Transformers, and Foundation Models adapted for geospatial Data. The application section lists the primary environmental monitoring tasks examined in the review, including wildfire prediction, flood monitoring, drought assessment, forest monitoring, land cover classification and change detection. The conclusion summarizes the results of the review by providing key insights identifying major challenges, limitations, and future research directions.</p>