Background & Aims Premenstrual syndrome (PMS) is a common disorder affecting 30-40% of women of reproductive age. Many of the modifiable risk factors associated with PMS involve nutrition and poor eating habits. This study aims to explore the correlation between micronutrients and PMS in a group of Iranian women and to build a predictor model showing the PMS using nutritional and anthropometric variables. Methods This is a cross-sectional which was conducted on 223 females in Iran. Anthropometric indices including Body Mass Index (BMI) and skinfold thicknesses were measured. For assessing the dietary intakes of participants, Food Frequency Questionnaire (FFQ) and for analyzing the data, machine learning methods were used. Results After applying different variable selection techniques, we’ve created machine learning models such as KNN. KNN achieved 80.3% accuracy rate and 76.3% F1 score indicating that our model is a curate and valid proof to show a strong relationship between input variables (sodium intake, Skin fold thickness of suprailiac, irregular menstruation, total calorie intake, total fiber intake, trans fatty acids, painful menstruation (dysmenorrhea), total sugar intake, total fat intake, and biotin) and the output variable (PMS). We sorted these effective variables based on their ‘Shapley values’ and we figured out Na intake, suprailiac skinfold thickness, biotin intake, total fat intake and total sugar intake have major impact on having PMS. Conclusions Dietary intakes and anthropometric measurements are highly associated with occurrence of PMS and in our model these variables can predict PMS in women with a high accuracy rate. Premenstrual syndrome (PMS) is a common disorder affecting 30-40% of women of reproductive age. Many of the modifiable risk factors associated with PMS involve nutrition and poor eating habits. This study aims to explore the correlation between micronutrients and PMS in a group of Iranian women and to build a predictor model showing the PMS using nutritional and anthropometric variables. This is a cross-sectional which was conducted on 223 females in Iran. Anthropometric indices including Body Mass Index (BMI) and skinfold thicknesses were measured. For assessing the dietary intakes of participants, Food Frequency Questionnaire (FFQ) and for analyzing the data, machine learning methods were used. After applying different variable selection techniques, we’ve created machine learning models such as KNN. KNN achieved 80.3% accuracy rate and 76.3% F1 score indicating that our model is a curate and valid proof to show a strong relationship between input variables (sodium intake, Skin fold thickness of suprailiac, irregular menstruation, total calorie intake, total fiber intake, trans fatty acids, painful menstruation (dysmenorrhea), total sugar intake, total fat intake, and biotin) and the output variable (PMS). We sorted these effective variables based on their ‘Shapley values’ and we figured out Na intake, suprailiac skinfold thickness, biotin intake, total fat intake and total sugar intake have major impact on having PMS. Dietary intakes and anthropometric measurements are highly associated with occurrence of PMS and in our model these variables can predict PMS in women with a high accuracy rate.