To validate the feasibility of laser-speckle imaging for detecting defects in snack food packaging, a laser-speckle measurement system is established to capture images of packaging with and without defects, such as bag swelling and air leakage. Gray-histogram features are extracted from preprocessed speckle images, including the mean value, variance, peak value, and skewness. Additionally, four features (angular second moment, entropy, moment of inertia, and correlation) derived from the gray-level co-occurrence matrix along with their corresponding standard deviations are extracted from the speckle images, thus resulting in 12 feature groups. Subsequently, these extracted features are classified using various models, including random forest (RF), k-nearest neighbor (KNN), support vector machine (SVM), binary logic regression (LR), and linear discriminant analysis (LDA). The results show that the RF model achieves the highest classification accuracy rate of 96.08%. Furthermore, by evaluating the feature importance via RF analysis and analyzing the single-feature quantities as well as the pairwise and three-three combinations of these quantities, we discover that the pairwise combinations yield the best performance with a classification accuracy rate of 94.12%. The overall test results indicate that combining laser-speckle imaging technology with machine vision is feasible for detecting defects in food packaging.