Cognitive distortion refers to the patterns of negative thinking which can distort a person’s perception of reality. These distorted thoughts lead to unhealthy behaviors, emotional distress, and mental health issues, like depression and anxiety. In order to detect cognitive distortion, Deep Learning (DL) techniques are employed; however, these approaches lead to a high error rate and poor performance. This is mainly because they fail to understand the hierarchical semantics, subtle emotional tones, and long-range dependencies within the text. Hence, a new model termed Hierarchical Attention Neural Harmonic Fusion Network (HAN-HFNet) is exploited for cognitive distortion detection from text. Initially, the input sentence is passed to Bidirectional Encoder Representations from Transformers (BERT) tokenization, which generates context-aware embeddings capable of capturing subtle emotional nuances, long-range dependencies, and hierarchical semantics critical for identifying cognitive distortions in text. Next, various Key Performance Indicators (KPIs), like Severity of Cognitive Distortions (SCD), Frequency of Cognitive Distortion (FCD), Correlation Between Cognitive Distortions and Depression Severity, Cognitive Behavioral Therapy (CBT), self-reports of cognitive distortions from individuals, Long-Term Monitoring of Cognitive Distortions (LT-MCD), and impact on daily functioning is considered. Lastly, the cognitive distortion is detected utilizing HAN-HFNet, which is obtained by integrating Hierarchical Deep Learning for Text classification (HDLTex) and Deep High-order Attention neural Network (DHA-Net) using harmonic analysis. This fusion enables the model to learn both coarse and fine-grained features, enhancing contextual understanding and reducing error. Moreover, the performance of the HAN-HFNet is evaluated using the Faulty Information Processing Dataset (FIPD), and it computed a minimum classification error of 0.072, and maximum recall, accuracy, precision, and F1-score of 94.756%, 92.754%, 91.866%, and 93.289%. Furthermore, the model is suitable for integration into real-world mental health support systems, offering scalability and potential deployment in online therapy platforms, clinical decision-making tools, and cognitive behavioral assessment frameworks.