Moisture content identification in soil is crucial for various applications in agriculture, construction, and environmental monitoring. Traditional methods for moisture detection often involve labor-intensive processes and specialized equipment which can be invasive, time-consuming, and expensive. This study explores use of spectrometry data, acquired through multispectral sensors using visible light and Near-Infrared (NIR) spectrum ranging from 400 to 1000 nm, for rapid and accurate moisture identification in soil and sand samples. The sensors leverage on-chip filtering to integrate up to eight wavelength-selective photodiodes into a compact 9 × 9 mm array, facilitating the development of simpler and smaller optical devices. The neural network model compromises of input layer, one hidden layer, and an output layer, developed using Tensorflow and Keras libraries. It was trained using the Adam optimizer and sparse categorical cross-entropy loss function for 35 epochs with a batch size of 16. Results indicate that the neural network model and appropriate classifiers can successfully classify soil moisture levels into 4 distinct categories based on given dataset, demonstrating its potential as a cost-effective and efficient alternative to traditional soil moisture measurement techniques.