Food allergies are medical conditions caused by particular immunological reactions brought on by exposure to certain foods. All age groups can experience food allergies, albeit the prevalence varies between children and adults, with children experiencing this condition more frequently than adults. Find food ingredients or substances that can trigger allergies, often known as allergens. This project attempts to determine whether or not the food includes allergies by applying the SVM data mining method to a public dataset of food goods and allergens that was acquired via Kaggle. High accuracy, effective memory use, and the ability to handle non-normally distributed data are some of the benefits of the SVM method. Data collection is the first step in the research process. Data pre-processing, which includes data transformation, handling missing values, and copy objects, comes next. Validation comes next. Split validation with 90% training data and 10% testing data, 10-fold cross validation, and split validation with an 80%–20% ratio were all compared in this study. The SVM method is applied after the dataset has passed validation, and the confusion matrix is used for the last evaluation step. SVM has an accuracy rate of 97.24% when using 10-fold cross validation, according to the accuracy value produced by the validation process comparison. Split validation yields an accuracy value of 97.50% when the ratio of training data to testing data is 90% to 10%. In contrast, an accuracy rate of 98.75% was achieved by using split validation with a ratio of 80% and 20%.