Background: Mental health problems often begin in adolescence, yet early detection and intervention remain limited. This study assesses the prevalence of psychological distress and its correlates among high-school students in Makkah, Saudi Arabia, and explores whether specific symptom clusters of depression, anxiety and bipolar/mania can be identified using the General Health Questionnaire‑30 (GHQ‑30). Methods: A cross‑sectional survey was conducted during the 2025–2026 academic year using stratified cluster sampling. A total of 535 students aged 15‑18 years completed a questionnaire containing the validated Arabic GHQ‑30 and demographic, socioeconomic and lifestyle items. The GHQ‑30 was scored with binary 0‑0‑1‑1 scoring (cut‑off ≥ 6) to define cases of psychological distress. Item clusters were used to screen for probable depression, anxiety and bipolar/mania. Descriptive statistics characterized the sample. Associations were examined using chi‑square tests and multivariable logistic regression. Results: Overall, 70.5 % of participants screened positive for psychological distress. The prevalences of probable depression, anxiety and bipolar/mania were 33.1 %, 28.2 % and 31.2 %, respectively. In adjusted models, female gender, insufficient sleep, lack of physical activity and exposure to bullying were associated with increased odds of psychological distress; longer sleep was protective. History of mental health conditions was a strong predictor of probable depression, whereas medication use was protective. Older age and higher paternal education were protective for anxiety. Bullying was the most consistent predictor across all symptom clusters. Conclusions: Psychological distress is highly prevalent among Makkah high-school students. Key determinants include gender, sleep duration, lack of physical activity and bullying. Routine school‑based mental health screening, sleep‑hygiene education, anti‑bullying initiatives and early referral pathways are warranted. Further research should examine and validate GHQ‑30 item clusters for specific disorders.