Sentiment analysis within the food industry offers essential insights into customer satisfaction, product perception, and emerging concerns. A novel sentiment classification model is developed for Amazon food reviews, leveraging sentiments are categorized as positive, neutral, or negative using techniques from Natural Language Processing and Machine Learning. Traditional ML algorithms, such as Logistic Regression, Naive Bayes, and Support Vector Machines, are combined with the BERT deep learning model to enhance classification accuracy. With a dataset of over 500,000 reviews sourced from Kaggle, the methodology includes data cleaning, feature extraction, exploratory data analysis, model training, and evaluation. Initial findings demonstrate SVM’s high predictive accuracy in sentiment classification, while BERT’s advanced contextual understanding suggests further enhancements. Applications of this model extend to real-time feedback systems that assist businesses in identifying and addressing customer sentiments promptly. Future developments aim to improve accuracy, incorporate a diverse range of datasets, and integrate real-time processing and multilingual analysis for broader, more effective sentiment analysis capabilities.