The need for artificial neural networks (ANN) with high computational performance and real-time pro-cessing capabilities has increased due to the rapid advances in science and technology. One of the most crucial components that makes an ANN function successfully is the Multiply Accumulate (MAC) Unit. The system's final total performance is determined by the MAC unit's performance. The basic operations often include additions and multiplications. The bulk of a processor's time and hardware resources are consumed by multiplying, compared to all other arithmetic op-erations, like as adding or subtracting. An examination of several MAC units with various multi-pliers and adders is conducted in this work. Adders are used to gather the incomplete products that are produced by multipliers. The Effective Mul-tiply Accumulate Unit (MAC) is crucial for real-time algorithms for processing that runs at peak speed. The increase in speed of MAC designs has been the major emphasis in recent years. This study analyzes several MAC units developed till date that are fast and energy-efficient.