###### Abstract  
Earthquakes are one of the most life-threatening natural phenomena, and their prediction is of constant concern among scientists. The study proposes that abrupt weather parameter value fluctuations may influence the occurrence of shallow seismic events by focusing on developing an innovative concept that combines historical meteorological and seismic data collection to predict potential earthquakes. A machine learning (ML) model utilizing the ML.NET framework was designed and implemented. An analysis was undertaken to identify which modeling approach, value prediction, or data classification performs better in forecasting seismic events. The model was trained on a dataset of 8766 records corresponding to the period from 1 January 2001 to 5 October 2024. The achieved accuracy of the model was 95.65% for earthquake prediction based on weather conditions in the Vrancea region, Romania. The authors proposed a unique alerting algorithm and conducted a case study that evaluates multiple predictive models, varying parameters, and methods to identify the most effective model for seismic event prediction in specific meteorological conditions. The findings demonstrate the potential of combining Internet of Things (IoT)-based environmental monitoring with AI to improve earthquake prediction accuracy and preparedness. An IoT-based application was developed using C# with ASPNET framework to enhance earthquake prediction and public warning capabilities, leveraging Azure cloud infrastructure. The authors also created a hardware prototype for real-time earthquake alerting, integrating the M5 Stack platform with ESP32 and MPU-605 sensors for validation. The testing phase and results describe the proposed methodology and various scenarios. earthquake detection; earthquake prediction; processing of seismic data; AI methods; machine learning; IoT infrastructure; prediction algorithm; ML.NET; weather parameters; Azure services 2020 20  
properties of megathrust faults influence rupture dynamics, leading to variations in fault slip and the radiation of seismic energy [2; 3]. Shallow earthquakes, typically occurring at depths of less than 15 km, are more likely to produce high-frequency seismic waves and trigger devastating tsunamis. In contrast, deeper earthquakes may exhibit slower rupture velocities and generate lower-frequency seismic emissions [4; 5]. The tectonic context has specific proper characteristics of earthquakes. Specific regions, such as North America's Cascadia subduction zone and Japan's Nankai Trough, display distinct seismic behaviors due to their unique tectonic environments. The Cascadia region, for instance, is characterized by complex seismic interactions, including shallow crustal earthquakes and deeper in-slab events, which influence the propagation of ground motion and affect seismic hazard assessments [6]. Meanwhile, the Nankai subduction zone is notable for episodic tremors and slow slip events, which can modulate the timing and magnitude of megathrust earthquakes [7]. The spatial and temporal patterns of seismicity also vary between regions. Research indicates that seismic activity within subducting slabs can migrate downward before large, shallow earthquakes, reflecting a complex interaction of seismic processes [8]. Slow earthquakes, marked by prolonged fault slips and low-frequency seismic waves, have been documented in numerous subduction zones. These events may contribute to the stress accumulation that precedes larger seismic events [9]. In an era of technological advancements, the intersection of artificial intelligence (AI) and seismology heralds a new frontier in earthquake prediction and monitoring. This article explores groundbreaking AI algorithms designed to analyze seismic data in real-time, enabling the detection of micro-earthquakes and aftershocks that precede major seismic events. Researchers can identify subtle patterns previously unnoticed by integrating machine learning (ML) models with vast datasets from global seismic networks.