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The recent developments in computational science and smart metering have led to a gradual replacement of the traditional load forecasting methods by artificial intelligent (AI) technology. The smart meters for residential buildings have become available on the market, and since then, various studies on load forecasting have been published. Contingency planning, load shedding, management strategies and commercialization strategies are all influenced by load forecasts. Predicting a lower load than the actual load results in utilities not committing the necessary generation units and therefore incurring higher costs due to the use of peak power plants; on the other hand, predicting a higher load than actual will result in higher costs because unnecessary baseline units are stated and not used . Artificial Neural Networks (ANNs) provide an accurate approach to the problem of energy forecasting and have the advantage of not requiring the user to have a clear, understanding of the underlying mathematical relationship between input and output. The aim of this work is to a carry-out Comprehensive Review of Artificial Neural Network Techniques Used for Smart MeterEmbedded forecasting System. Keywords: Artificial Neural Network, Smart Meter, Artificial Intelligent, Energy, forecasting.