This study investigates the potential of multimodal data integration, which combines electroencephalogram (EEG) data with sociodemographic characteristics like age, sex, education, and intelligence quotient (IQ), to diagnose mental diseases like schizophrenia, depression, and anxiety. Using Apache Spark and convolutional neural networks (CNNs), a data-driven classification pipeline has been developed for big data environment to effectively analyze massive datasets. In order to evaluate brain activity and connection patterns associated with mental disorders, EEG parameters such as power spectral density (PSD) and coherence are examined. The importance of coherence features is highlighted by comparative analysis, which shows significant improvement in classification accuracy and robustness. This study emphasizes the significance of holistic approaches for efficient diagnostic tools by integrating a variety of data sources. The findings open the door for creative, data-driven approaches to treating psychiatric diseases by demonstrating the potential of utilizing big data, sophisticated deep learning methods, and multimodal datasets to enhance the precision, usability, and comprehension of mental health diagnostics.

-- Individuals' general well - being is greatly impacted by mental health conditions including depression and Post - Traumatic Stress Disorder (PTSD), underscoring the importance of early detection and precise diagnosis in order to facilitate prompt clinical in tervention. An advanced multimodal deep learning system for the automated classification of PTSD and depression is presented in this paper. Utilizing textual and audio data from clinical interview datasets, the method com - bines features taken from both mo dalities by combining the architectures of LSTM (Long Short - Term Memory) and BiLSTM (Bidirectional Long Short - Term Memory).Although text features focus on speech's semantic and grammatical components; audio features capture vocal traits including rhythm, t one, and pitch. This combination of modalities enhances the model's capacity to identify minute patterns connected to mental health conditions. Using test datasets, the proposed method achieves classification accuracies of 92% for depression and 93% for PT SD, outper - forming traditional unimodal approaches and demonstrating its accuracy and robustness. In addi - tion to lowering people's quality of life, many illnesses have a significant negative impact on society and the economy. If not treated or recognized, mental health issues can lead to chronic diseases, decreased functioning, and even higher death rates. In under - resourced areas mental health issues are prevalent, even with advancements in clinical practice, traditional methods of diagnosing these disorders -- such as psychological testing and in - person interviews -- are still limited due to their subjective nature, resource - intensive nature, and reliance on the availabil - ity of qualified healthcare professionals.