Data aggregation is the process of efficiently collecting and transferring sensing data to a destination; it aids in the methodical and cautious utilization of sensor network resources. In the same way, when opening aggregated data that is free of noise and errors, the accuracy and efficiency of the data received improve. For this to happen, the data must flow to the destination in the most precise manner and in the shortest possible time by understanding the area's circumstances and addressing the immediate demands of the site where the sensors are positioned. This necessitates precise algorithms that eliminate errors and noise in the sense of data before aggregating it. The network used the ACNM technique – accurate data aggregation created by neural networks and data classification processed through machine learning in wireless sensor networks. We employ machine learning as part of artificial intelligence that learns from data. Following that, it can detect errors and noise in that Data in stages, reduce them, process them in a neural network model, and finally aggregate them to deliver the most accurate data to the destination. When data was provided using this ACNM protocol, the results showed that it arrived at its destination with the least delay.