Advancements in AI-Driven Detection and Monitoring of Depression and Anxiety using Multimodal Digital Biomarkers and Behavioural Data

Depression and anxiety are two of the world's most common mental health disorders, commonly underdiagnosed because conventional evaluation is largely based upon patient self-report and clinical observations. With growth in intelligent computing technologies, artificial intelligence (AI) and multimodal sensing, new avenues for accurate, non-invasive, and scalable mental health evaluation have opened. This paper delves into a technology environment that utilizes digital biomarkers, facial expression, voice, physiological signals, textual behavior, and wearable sensors—and machine learning and deep learning methodologies for bolstering early detection and real-time monitoring of depression and anxiety. Based on IEEE-indexed research, this paper considers various AI models such as convolutional neural networks, LSTM networks, ensemble models, and transformers for various datasets from social media, remote interviews, EEG signals, speech from smartphones, and physiology logs. All these methods have been proven to have classification accuracies ranging up to 99% and AUROC scores higher than 0.80 across various experiments. The survey involved systematizing more than 20 prominent papers, comparing model performance, datasets, and multimodal features employed. The analysis result indicated that multimodal integration between behavioral and physiological modalities provides stronger predictive performance compared to unimodal systems. Speech analysis from smartphones, eye movement entropy measures, and wearables-based HRV analysis were found useful for real-time mental health tracking. This article puts AI-based multimodal platforms forward as instrumental for future clinical and community-based mental health interventions, lowering bias, increasing adoptability, and enabling timely interventions.