Europe, as the world’s leading tourist destination, offers a wide range of luxury hotels. However, discrepancies often exist between the perceived quality of these accommodations and the ratings they receive on popular review platforms. Differences in cultural definitions of luxury and potential biases, such as paid ratings, further complicate the reliability of online feedback. To address this, the present study conducts sentiment analysis and exploratory data analysis on hotel reviews from 14 European countries—Belgium, Italy, Portugal, Germany, France, Spain, Netherlands, Switzerland, Norway, Sweden, Finland, Poland, Czechia, and England. The dataset comprises approximately 4,800 reviews, sourced from TripAdvisor (37%) and Google Reviews (63%). Six machine learning algorithms were employed for sentiment classification: Logistic Regression, Random Forest, Decision Tree, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Naïve Bayes. Among these models, Logistic Regression achieved the highest accuracy of 93.76%, demon starting its effectiveness in sentiment prediction. The analysis also revealed that Portugal, particularly the Corpo Santo Lisbon Historical Hotel, received the most favorable reviews. These findings offer valuable insights into customer satisfaction trends across the European luxury hospitality sector.