Accurate long-term load forecasting is very important for electric utilities in planning for new plants. Also it is very significant for the routine of maintaining, scheduling annually, electrical generation, and loads. The paper presents the design of two models for long-term forecasting electricity load since year 2025. The first model is for total demand forecasting whereas, the second model is for sectors demand forecasting using Adaptive Neuro-Fuzzy Inference System (ANFIS).The paper defines the load forecast types and the summary of the most important factors affecting the load forecast in Egyptian electricity network. The research work presents the deferent analysis between the two models results. Results and forecasting performance obtained reveal the effectiveness of the proposed approach and shows that it is possible to build a high accuracy model with less historical data using a combination of neural network and fuzzy logic.