Authors - Aditya Bhabal, Aditi Bharimalla, Shruti Balankhe, Vaibhav Chavan, Vaibhav Narawade Abstract - The overnight growth of OFD service businesses, due to technological advancement and a change in consumer behavior, has made reviews furnished by customers imperative for improvement in service quality, demand forecasting, and customer satisfaction. The vast amount of unstructured data makes the conventional method too ineffective. The following review thus provides valuable insights from diverse studies that are being done to apply deep learning, reinforcement learning, and ensemble learning in analyzing customer reviews of food delivery platforms. It goes on to provide ways through sentiment analysis, demand forecasting, dynamic recommendations of orders, and personalized marketing that these studies have proven how machine learning can make a difference in operatively effectively producing efficiency. It also provides an overview of the challenges in terms of data imbalance, scalability, and sustainability concerns, thus showing perspectives for further research in developing OFD platforms' capabilities for optimized and personalized services that take into account environmental and social impacts.
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