<p>The increasing frequency and intensity of natural disasters, such as floods, cyclones, earthquakes, and wildfires, have highlighted the urgent need for intelligent and proactive disaster management systems. Traditional monitoring approaches often suffer from delayed responses, fragmented data collection, and limited predictive accuracy, thereby exposing vulnerable populations to significant risks. The integration of the Internet of Things (IoT) and Artificial Intelligence (AI) has emerged as a transformative approach for addressing these challenges by enabling real-time environmental monitoring and predictive disaster alerts.</p>

<p>This study explores IoT- and AI&ndash;empowered intelligent applications designed for environmental monitoring with a focus on disaster prediction and early warning systems. A dataset comprising 200 data points was generated to simulate IoT sensor readings, including parameters such as temperature, humidity, rainfall, wind speed, seismic activity, and air quality. AI-driven models were applied to this dataset to identify patterns, establish correlations, and predict potential disaster events. Descriptive statistics, correlation analysis, and predictive modelling using machine learning algorithms were employed to interpret the data.</p>

<p>This research contributes to the growing field of intelligent disaster management by presenting an evidence-based framework for IoT and AI applications in environment monitoring. The proposed approach holds promise for improving community resilience, minimizing economic losses, and saving lives through timely and accurate disaster alerts.</p>