Depression represents a significant global public health challenge, with diagnostic accuracy hindered by the limitations of subjective clinical assessments. To address these shortcomings, this study systematically applies advanced Machine Learning (ML) and Deep Learning (DL) methodologies for the enhanced detection, assessment, and forecasting of depressive disorders. The analysis integrates heterogeneous data sources, including social media activity and behavioral metrics, facilitating the identification of nuanced patterns and interrelationships through rigorous Exploratory Data Analysis (EDA). Insights from EDA informed a refined data preprocessing pipeline, with Principal Component Analysis (PCA) employed to optimize feature dimensionality and suppress noise. The study implements both traditional classifiers e.g. Support Vector Machines (SVM) and Random Forests—and state-of-the-art deep learning models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) networks. These models are designed to discern verbal, emotional, and behavioural markers associated with depression. Model efficacy is comprehensively evaluated using established performance metrics: accuracy, precision, recall, F1-score, and AUC-ROC, ensuring the robustness and validity of the investigational outcomes. Furthermore, by establishing a scalable, data-driven framework to assist mental health professionals in more precise diagnosis and explanation of depression, this research aims to improve early detection and facilitate timely intervention.