### Internet of Things Applications  
Integrating the Internet of Things (IoT) into earthquake monitoring and early warning systems has introduced a transformative approach to improving disaster preparedness. Through real-time data collection and analysis, IoT technologies enhance the detection and notification of seismic events, thereby reducing potential damage and loss of life. The IoT infrastructure in earthquake early warning systems (EEWS) uses low-cost sensors for real-time seismic data acquisition. For example, the BLESeis sensor system has been developed to improve earthquake detection and notification capabilities, addressingchallenges related to high-fidelity sensing and classification accuracy [51]. Complementing these sensors, ML algorithms analyze the collected data to estimate the seismic intensity and predict ground motion in real-time, ensuring timely alerts [52; 53]. This combination of technologies allows for a more expansive and responsive seismic network, supporting the rapid assessment of earthquake severity and applying risk-reduction strategies [54]. IoT architectures also enable the deployment of distributed sensor networks that can communicate seamlessly. [PERSON] et al. [55] outline using MEMS sensors and AI within an IoT framework to build a dense seismic network for earthquake monitoring. In the same way, [PERSON] et al. [56] integrate MPU6050 sensors with NodeMCU to monitor seismic activity and floods, sending alerts via GSM. [PERSON] et al. [57] expand on this by presenting a comprehensive IoT-based disaster management system that improves earthquake preparedness, response, and recovery. [PERSON] et al. [58] also describe South Korea's effort to deploy a nationwide EEW system using thousands of MEMS sensors and DL algorithms to enhance real-time earthquake detection. An example is using MEMS-based accelerometers in mobile devices, which continuously monitor ground vibrations and trigger alerts based on predefined algorithms [59]. Such systems improve the precision of earthquake detection and ensure the quick dissemination of alerts to affected populations, thereby contributing to public safety [60; 61]. Additionally, integrating cloud computing with IoT enhances these systems' processing and storage capacities, facilitating the management of vast data volumes generated during seismic events [62]. Beyond traditional seismic sensors, innovative approaches such as smartphone-based networks are being explored for earthquake detection. The MyShake project, for instance, uses the sensors embedded in smartphones to create a crowdsourced seismic network, expanding the coverage and responsiveness of earthquake monitoring [61; 63]. Disaster management systems are evolving to integrate real-time monitoring and early warning capabilities. IoT-based networks allow more effective resource allocation and emergency response during earthquakes. [PERSON] et al. [57] highlight the importance of reducing false alarms in earthquake warning systems to maintain credibility and minimize unnecessary disruptions. The main contributions of the paper are as follows:  
* Introduce the original concept of integrating weather parameter analysis and AI-driven predictive modeling for earthquake prediction;
* Collect and analyze historical meteorological and seismic data;
* Design and implement the ML model using the ML_NET framework and proposed the alerting algorithm;
* Analysis to identify which modeling approach, value prediction, or data classification provides insights into the optimal method for seismic forecasting;
* Implement a case study regarding multiple models by varying the parameters and the methods;
* Develop an IoT-based application designed to enhance earthquake prediction and public warning using Azure cloud infrastructure;
* Develop the hardware prototype for real-time earthquake alerting by integrating MSStack with ESP32 and MPU-6050 sensors for validation purposes;
* Conduct the validation process. The paper is structured into five sections.