Introduction: Strawberry quality is one of the most important factors that guarantee consistent commercialization of the fruit and ensures the consumer's satisfaction. However, assessing their sensory quality is a costly task. Thus, this work employs physical (height, diameter, firmness, juice yield, the color of the fruit, and the color of the juice) and physical-chemical (pH, soluble solids, and titratable acidity) data to predict the sensory quality of strawberries. Objective: This work's objective was to generate RF-based models to predict the acceptance, expectation, ideal of sweetness, ideal of acidity, and the ideal of succulence based on the physical and physical-chemical data. These predicted values were used as input for another RF-based classification model, which classifies the strawberry samples regarding the consumer's satisfaction and paying intention. Methodology: A total of 715 consumers evaluated 30 strawberry samples according to their ideals of sweetness, acidity, and succulence, and regarding their acceptance, expectation, satisfiability, and pay intention. This work uses Random Forest (RF) to predict the ideals, the acceptance, and the expectation using physical and physical-chemical variables. Then, these predicted values were used to classify the samples in “satisfied” or “not satisfied” and “would pay more” or “wouldn’t pay more”. The data was separated into training and validation groups and the parameters of the RF algorithm were optimized using random search. Results: The RF achieved a coefficient of determination R² > 0.72 and a root-mean-squared error (RMSE) smaller than 0.17 for the prediction task, which indicates that one can correctly estimate the sensory measures of strawberries using physical and physical-chemical data. The RF was able to correctly classify 87.95% of the strawberry samples in the classes “satisfied” and “not satisfied” and 78.99% in the classes “would pay more” or “wouldn’t pay more”. Conclusion: The results indicate that the developed models can be used in the quality control of strawberries, supporting the stablishment of quality standards that consider the consumer’s response. Additionally, it indicates that one can use the physical and physical-chemical data of other fruits to predict their sensory quality using the proposed methodology.