Avalanche hazard prediction remains a crucial task for mountainous regions worldwide. This study presents the development and field testing of a prototype automated avalanche hazard monitoring system designed for the East Kazakhstan region. The system integrates a snow avalanche station (including temperature, humidity, and pressure sensors; a magnetoelectric wind sensor; a data logger; and devices for autonomous operation), a temperature snow measuring rod, an API (application programming interface) service for storing weather and climate parameters in a database, and a web interface. Powered by autonomous solar energy solutions, the system ensures continuous, high-resolution monitoring of key environmental parameters. Using initial test datasets, we analyzed the specific strengths and weaknesses of the developed monitoring system using the example of one avalanche site. Avalanche prediction was performed using regression analysis (logistic regression). The evaluation of the model showed a high forecasting accuracy, with recognition rates exceeding 98%. The obtained regression coefficients can be used to predict avalanches based on meteorological data collected using the proposed equipment. The developed solution holds significant promise for improving avalanche risk management practices and can be expanded for broader application in both national and international contexts.