Early diagnosis, monitoring, and effective diagnostics of abnormal speech can greatly benefit from computerized acoustic examination. This study presents a methodology for identifying voice pathology by employing Singular Value Decomposition (SVD) datasets for feature extraction. Acoustic measurements are utilized to assess the health of the voice, and the accuracy of these parameters influences speech noise detection methods. The extracted features are then processed through a system consisting of 27 neural network layers, comprising Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). A 10-fold validation technique is employed to split the dataset into training and testing datasets, enabling the evaluation of the system's performance. Experimental results demonstrate an observed accuracy of 87.11% for the CNN model and 86.52% for the RNN model. The classifier's performance is further assessed using 10-fold cross-validation. The implementation of the system's program is carried out in Python, utilizing the TensorFlow package, and computations are executed on a single NVidia Titan X GPU. The Linux operating system is employed for all computations. The proposed methodology offers promising potential in the early identification and monitoring of voice pathologies, providing a valuable tool for healthcare professionals in the field of speech analysis and diagnostics.