The increasing frequency of natural disasters and environmental pollution incidents in recent decades has posed significant challenges to crisis management and public health, rendering traditional environmental monitoring methods—primarily reliant on manual sampling—insufficient due to inherent temporal and financial constraints. This narrative review explores the pivotal role of emerging technologies, specifically Artificial Intelligence (AI), the Internet of Things (IoT), and Remote Sensing, in enhancing the speed and precision of environmental health assessments during crisis scenarios. A comprehensive literature search was conducted across international databases, including Scopus, PubMed, Web of Science, and ScienceDirect, spanning from 2018 to 2025. Analysis of the gathered evidence indicates that the convergence of AI and environmental sensors has shifted the monitoring paradigm from a reactive to a proactive approach; notably, fluorescent sensor arrays integrated with machine learning algorithms can identify toxic arsenic species with an accuracy exceeding 96%, while Unmanned Aerial Vehicles (UAVs) equipped with multispectral sensors and Convolutional Neural Networks (CNNs) have increased the accuracy of post-earthquake structural damage assessments to over 85%, reducing hazard mapping time by up to 80%. Furthermore, deep learning-based super-resolution techniques have significantly improved the clarity of satellite imagery for monitoring urban groundwater aquifers. The findings demonstrate that smart technologies bolster the resilience of environmental health systems through real-time monitoring and the reduction of human error, although the development of integrated platforms and global standardized protocols remains essential to address data challenges and cybersecurity concerns.