The reconstruction of weather data is essential for various applications such as weather forecasting, climate research, and disaster preparedness. Traditionally, this task required multiple instruments to record different attributes, posing challenges for complete data reconstruction. In this study, we have proposed a simple yet effective approach based on graph embedding and independent features to reconstruct the entire family of weather attributes. Exploiting weather histories from 62 stations across diverse climate regions in Nepal, our method enables the imputation of temperature and humidity data for specific weather stations as well as all stations over a period of time. Rigorous testing and validation demonstrate the effectiveness of our approach, with key evaluation metrics including Mean Squared Error (MSE), Mean Absolute Error (MAE), and coefficient of determination (R2). Our results highlight the model’s proficiency in reconstructing comprehensive weather data, offering a promising avenue for enhancing the reliability of weather-related applications. Also, the use of graph embedding techniques and independent features, our approach provides a robust framework for reconstructing historical weather data, addressing the challenges associated with incomplete or fragmented datasets.