A common neurodevelopmental issue, attention-deficit or hyperactivity disorder (ADHD) impairs a child’s cognitive, emotional, and behavioral development and frequently results in long-term challenges in education and with others. The possibility of neurofeedback (NF) as a non-pharmacological treatment for ADHD symptoms is examined in this study, which also uses machine learning approaches to increase the precision of diagnosis and customize treatment. Electroencephalogram (EEG) signals were recorded from a cohort of children clinically diagnosed with ADHD. Feature selection and channel optimization were performed using support vector machines (SVM) in combination with independent t-tests, narrowing the focus to six critical EEG channels that exhibited strong associations with ADHD-related brain activity patterns. To evaluate the effectiveness of NF therapy, a meta-analysis was conducted using data from previously published clinical studies. The analysis revealed measurable improvements in attention control, reduced hyperactive behavior and decreased impulsivity. In parallel, several machine learning classifiers including Random Forest, k-nearest Neighbors, Decision Tree, and Logistic Regression were trained on the extracted features. Among these, the Gaussian Process model using a Radial Basis Function kernel demonstrated the highest predictive capability, achieving a recall close to ninety-eight and a half and an overall accuracy nearing ninety-seven and a half. These findings underscore the value of integrating neurofeedback with computational intelligence and EEG-based monitoring for enhanced ADHD assessment and therapeutic outcomes in pediatric populations.