Despite being among the most common psychological disorders, anxiety-related conditions are still primarily identified through subjective assessments, such as clinical interviews and self-evaluation questionnaires. These conventional methods often require significant time and may vary depending on the evaluator. However, the emergence of advanced artificial intelligence techniques has created new opportunities for detecting anxiety in a more consistent and automated manner. To address the limitations of traditional approaches, this study introduces a comprehensive model that integrates deep learning architectures with optimization strategies inspired by swarm intelligence. Using multimodal and wearable-sensor datasets, the framework analyzes physiological, emotional, and behavioral signals. Swarm intelligence techniques including genetic algorithms and particle swarm optimization are incorporated to refine the feature space and optimize hyperparameters. Meanwhile, deep learning components are tasked with deriving layered and discriminative representations from sequential, multi-source inputs. Our evaluation shows that the fusion of these two computational paradigms significantly enhances detection performance compared with using deep networks alone. The hybrid model achieves notable improvements in accuracy and demonstrates stronger generalization across various individuals. Overall, the results highlight the potential of combining metaheuristic optimization with deep learning to develop scalable, objective, and clinically meaningful solutions for assessing anxiety disorders