Weather forecasts are the process of predicting the conditions of the atmosphere at a particular location and time. For various industries such as agriculture, transportation and energy, accurate weather forecasts are essential to make informed decisions and minimize possible damage caused by weather-related events. Our projects are designed and developed to provide accurate forecasts. The objective of this model is to integrate several sensors. The proposed system combines a small-scale multi-sensor scheme (temperature, humidity, pressure and gas) with an open-source edge computing framework. The main objective of the project is to develop a weather forecast system using time series analysis and ARIMA algorithms. The system will use this algorithm to identify patterns in historical weather data and predict future weather conditions. The system uses historical weather data to forecast future weather conditions such as temperature, humidity, pressure and rainfall. Our project includes data collection, data preprocessing, modeling training and evaluation. The collected weather data will be processed in advance to eliminate deviations and missing values. The ARIMA algorithm is strained with preprocessed data and the performance of the system is evaluated using a metric such as the mean square error and root mean square error (RMSE). The final output of the systemic weather forecast for a particular location, date and time. We will be able to provide a reliable low-cost alternative to each sector we use. The proposed platform provides and visualizes information to user devices in a world-wide scenario in a complete-to-end manner.