This study aims to investigate the non-cognitive factors influencing the recognition of early stroke symptoms among Korean working class males with diabetes using an integrated machine learning approach combining Multi-Output Gradient Boosting and logistic regression models. Data from the Korea National Health and Nutrition Examination Survey (KNHANES) from 2016 to 2022 were utilized, including 4125 working class males with diabetes. Participants were divided into two groups based on their recognition of early stroke symptoms. The integrated machine learning model was trained on 80% of the dataset and tested on the remaining 20%. Key predictors were identified, and logistic regression analysis provided odds ratios (OR) and 95%confidence intervals (CI) for significant factors. The study found that 72% of participants recognized early stroke symptoms, while 28% did not. Significant predictors of non-recognition included younger age (β = −0.05, OR = 0.95, p < 0.01), higher Body Mass Index (BMI) (β = 0.12, OR = 1.13, p < 0.01), hypertension (β = 0.28, OR = 1.32, p < 0.01), elevated cholesterol (β = 0.03, OR = 1.03, p < 0.01) and triglycerides (β = 0.04, OR = 1.04, p < 0.01), depression (β = 0.25, OR = 1.28, p < 0.01), stress (β = 0.18, OR = 1.20, p < 0.01), smoking (β = 0.10, OR = 1.11, p < 0.01) and alcohol consumption (β = 0.08, OR = 1.08, p < 0.01). Positive factors included regular physical activity (β =−0.20, OR = 0.82, p < 0.01) and participation in diabetes education programs (β = −0.15, OR = 0.86, p < 0.01). The findings highlight the multifactorial nature of stroke symptom recognition and suggest that targeted interventions focusing on both physiological and psychological factors, as well as promoting healthy lifestyle behaviors, can significantly improve symptom recognition and health outcomes in working class males with diabetes.