• Electric load forecasting has a major impact on the efficiency of the power network. • Using the MOO model, this research proposed a new electric load prediction technique. • The attributes of historical records were first summarized using an EMI technique. • The GRNN is then employed to forecast the electric load in the forecasting module. • To fine-tune the hyperparameters, a fuzzy based GEO strategy is used. The effectiveness of the power network system's activities and financial gains are significantly impacted by electric load forecasting. This domain offers a wide range of capacity prediction models to improve prediction performance; nonetheless, precise forecasting models are required. Therefore, this research proposed novel Electric Load Prediction Techniques for Rajasthan Region and Suggestive Measures for Optimal Energy using the Multi-Objective Optimization model. Specifically, data preparation, a subset of features component, a training prediction subsystem, and a management system that helps make up our integrative system. An enhanced mutual information technique was first used to summarize historical records qualities. Then, the Generalized Recursive Neural Network (GRNN) is used to predict the electric load values in the training and forecasting module. Furthermore, a Fuzzy based Golden eagle optimization approach is employed to fine-tune the mode's hyper parameters. Moreover, the proposed model's MAPE is lower when compared to the benchmark models. Our UMI-based model has a reduced MAPE error of 0.4920%, which enhances prediction performance by minimizing error. The research findings indicate that the constructed approach can be applied as a technique for electrical grid programming and has considerable advantages over most specific products.