Swathi Priya and Swetha Reddy 01 01 Based on the networks modifications and the type of data it perceives the machine learning system was specifically created to save energy and extend sensor life Additionally by combining similar types of data that are sensed inside the network coverage region the sensor network is created using machine learning and the BAT computational approach to minimise the network under redundancy Additionally the network uses very little energy from the sensors therefore feature sets in fuzzyneuron machine learning mode are employed to choose neighbours Furthermore the packet speed sensed and the detecting intervals between packets are used to calculate the energy used by the sensor in the network Additionally the neural network is used to open and aggregate the data The approach designed to reduce the aggregation methods energy use The same data is then aggregated once that is finished removing the noise from the data This lowers energy consumption and increases the use of network resources Furthermore a routing path that optimises during the routing path development process is created using BAT computation resulting in a consistent and energyefficient path Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026