Fresh fruits and vegetables need transportation and storage after harvesting, due to the regional and seasonal production in recent years. It is a high demand to clarify the relationship between environmental factors and the sensory quality of fresh fruits and vegetables during transportation. Accurate and rapid evaluation and prediction can be of great significance to maintain the logistics and sensory quality of fresh fruits and vegetables using environmental factors. Taking the table grape as the research object, the transportation simulation and sensory experiments were carried out in the laboratory, according to the tracking and monitoring actual transportation process. A data set of sensory quality was then constructed for the table grape during transportation. A prediction model was established for the environmental factors (temperature, relative humidity) during transportation and sensory quality (appearance, aroma, texture and flavor) using the Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) with the Multiple Output Support Vector Regression (PSOGA-MSVR) model. Specifically, the SVR model was widely used to identify the small sample, nonlinear, and high-dimensional pattern in the field of food quality prediction. The accuracy and generalization of SVR model depended largely on the kernel function and hyperparameters. The Radial Basis Function (RBF) was selected as the kernel function in this case. The results show that the PSOGA joint optimization effectively improved the parameter adjustment efficiency of the MSVR model. The improved PSOGA-MSVR model performed better prediction under three transportation modes, including normal, cold temperature, and cold chain transportation. Among them, the Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and coefficient of determination (R2) of the model were 0.083, 0.029, 0.172, and 0.982, respectively, under the normal temperature transportation. The MAE, MSE, RMSE, and R2 were 0.077, 0.022, 0.148, and 0.981, respectively, under cold storage and transportation. The MAE, MSE, RMSE, and R2 of cold chain transportation were 0.063, 0.018, 0.138, and 0.985, respectively. Therefore, cold chain transportation achieved the highest prediction accuracy for the sensory quality of table grapes. The better-fitting model was attributed to the longest time series and the most abundant data in the cold chain data set. There was a significant nonlinear relationship between transportation environmental factors and grape sensory quality (appearance, aroma, texture and flavor). Consequently, reasonable regulation of transportation environmental factors can be expected to effectively maintain the sensory quality of fresh fruits. The SVR model can also be used to simulate the logistics environmental factors and grape sensory quality of fresh table grapes. The quality evaluation and prediction can provide a theoretical, technical, and practical solution to improve the logistics process and quality of fresh table grapes.