The test results showed that the suggested solution had the best classification accuracy (90.07%) [59]. Various approaches of neural networks and deep neural networks have been used for classification of speech quality and voice disorders with very promising results [43; 44; 45; 46; 60; 61]. New prospects are presented to assist clinical decision-making through the use of AI algorithms, automated instruments for measuring, decision-making, and classification in communication deficiencies and NDs in the research setting [11; 12; 13; 14; 15; 62]. Traditional ML approaches use separate feature extraction procedures and classification methods, but with Deep Learning these two procedures are done comprehensively [42]. For the ASD diagnosis in young children from 5 to 10 years old, an intelligent model has been presented based on resting-state functional magnetic resonance imaging data from global Autism Brain Imaging Data Exchange I and II datasets and using convolutional neural networks (CNNs) [63]. The best results have been obtained with Adamax optimization technique. A review of ML research for MRI-based ASD identification deduced that the accuracy of research studies with a significant number of participants is generally lower than that of studies with fewer participants, implying the further need for large-scale studies [64]. Regarding participants' age, it is shown that the accuracy of ASD automated diagnosis is higher for younger individuals [64]. Another thorough examination of deep learning approaches looks into the prognosis of neurological and neuropsychiatric disorders, reporting more potential for diagnosing stroke, cerebral palsy, and migraines using various deep learning models [65]. A deep neural network model employed in the early screening of ASD, assessing children's eye tracking data applicability, reported outcomes that strongly indicated efficiency in helping clinicians for a quick and reliable evaluation [15]. The outcomes of a review article on ML methods of feature selection and classification for ASD, used to analyze and investigate ASD, indicate an improvement in diagnostic accuracy, time, and quality without complexity [66]. In an analysis and detection of ASD after applying various ML techniques and handling missing values, the results strongly suggest that convolutional neural network- (CNN) based prediction models work better on their datasets with a significantly higher accuracy for ASD screening in children, adolescents, and adult data [67]. A CNN is employed for the classification of ADHD, trained with EEG spectrograms of 20 patients and 20 healthy participants. The model has an accuracy of 88% \(\pm\) 1.12%, outperforming the Recurrent Neural Network and the Shallow Neural Network, with the advantage of avoiding the manual EEG spectral or channel features [68]. Furthermore, a CNN was used to identify ADHD from a dataset of children (ADHD: 50, Healthy: 57) and the network input data consisted of power spectrum density of EEGs. The accuracy obtained was 90.29% \(\pm\) 0.58% [69]. Additionally, serious games which embed fine motor activities obtained from a mobile device and deep learning convolutional neural networks (CNN) are proposed as novel digital biomarkers for the classification of developmental disorders [12]. A pilot study of an integrated system that includes a serious game and a mobile app, and utilizes ML models that measure ADHD behaviors, suggests their significant potential in the domain of ADHD prediction [14]. Moreover, a gamified online test and ML using Random Forests for the predictive model were designed with results revealing that their model correctly detected over 80% of the participants with dyslexia, pointing out that dyslexia can be screened using an ML approach [62]. Consequently, more in-depth research is needed which utilizes automatic classification techniques to assist clinicians' decision making.