Bread is a basic food consumed by billions of people worldwide. Although bread is consumed in large quantities, its freshness is short-lived. Due to its susceptibility to spoilage and potential health risks, it is vital to ensure its safety before consumption. With the increasing demand for efficient quality control in the food industry, the application of machine learning algorithms has gained significant attention. In our research, we investigate the effectiveness of ensemble machine learning techniques in improving the prediction of bread spoilage and compare their performance with single machine learning algorithms. A diverse dataset of fresh and rotten bread images was meticulously collected for the study using the Pixel 7a smartphone. A two-pronged approach was employed for food spoilage detection. The first compared ensemble methods (Adaboost, Gradient Boost, XGBoost, Bagging) with individual algorithms (Random Forest, LDA, Naive Bayes, Decision Tree, Logistic Regression, SVM, and KNN) using basic pixel intensity-based classification. The second approach extracted three feature categories: intensity, texture, and edge, to train the algorithms. Key metrics like accuracy, AUC, precision, recall, and F1-score were used for evaluation. The results revealed the performance of each algorithm in discriminating between fresh and spoiled bread. Notably, XGBoost achieved the highest overall accuracy of 91.8%, with strong precision, recall, F1-score, and AUC of 96%. Random Forest also performed well with high accuracy (0.8979) and AUC (0.9610). While these models showed promising results, the optimal choice depends on the application specifications. This comparative analysis provides valuable insights for selecting suitable machine learning algorithms and underscores their potential for optimizing freshness monitoring across various perishable foods.