This research aims to investigate the input factors influencing e-waste generation and analyze their predictive capabilities using linear regression techniques and neural networks. The study utilizes data collected from the population, Gross Domestic Product (GDP), inflation, and the amount of e-waste obtained from the Pollution Control Department. By comparing the performance of linear regression and neural networks, this research seeks to identify the most effective approach for modeling and forecasting e-waste generation based on the selected input factors. The findings will contribute to improved understanding and prediction of e-waste patterns, aiding policymakers and waste management authorities in developing sustainable strategies for e-waste management. The data were forecast the possible volume of e-waste in the future. A model using neural network modeling techniques be dividing data into different layers:3 input layers,3 hidden layers,1 bias, and 1 output layer.The effect of deep learning is to get a Learning Rate: LR = 0.01 that doesnt increase the loss value. This allows the model to be trained as fast as possible, with a loss of0.0056, Os 92 ms/step. Neural Network learned with data, and reworked it to fit this data 500 times (Epochs = 500), where Root Mean Squared Error: RMSE= 0.0751, RMSE approaches 0, showing that the model is highly accurate.