Noise in images is a prevalent issue in digital image processing that reduces the reliability and accuracy of visual data. Conventional noise classification methods frequently depend on spatial domain characteristics, which may not be adequate to capture all types of noise. The current research introduces a unique approach that combines frequency domain analysis with machine learning classifiers to categorize different kinds of visual noise. 2700 pictures that were created by introducing 10 different forms of noise on immaculate photographs made up the training set. The visual noise is evaluated using the Fourier Transform and additional frequency domain methods. Classification is then done using Random Forest (RF) and Support Vector Machine (SVM) classifiers. In evaluation, individual noise types like Motion, Gaussian, Impulse, and Speckle are used; composite noise types include combinations like Gaussian-Motion, Gaussian-Impulse, Gaussian-Speckle, Impulse-Speckle, and Speckle-Motion. The performance of the proposed method is evaluated using Accuracy. Random Forest performs better than Support Vector Machine when compared to other frequency algorithms. The method increases the noise classification’s robustness and may find application in real-time image processing systems where accurate noise characterization is required for tasks like augmentation or restoration.