Abstract: Due to national policies and adjustments to the industrial structure, the tobacco industry has implemented "quality optimization, planting regionalization, and technological intelligence" process requirements. To better meet these requirements, understand the quantitative relationships between tobacco chemical components and ecological factors, and improve the intelligence degree of flue-cured tobacco quality evaluation, it is necessary to develop an ecological prediction model of the chemical composition of tobacco leaves that corresponds with the actual production of Yuxi flue-cured tobacco. While prior research has only considered the impact of a single ecological factor (weather or soil) on the chemical composition of tobacco leaves, this study used the main chemical components (nicotine, total sugar, reducing sugar, total nitrogen, potassium, and chlorine) of flue-cured tobacco 'K326' in nine typical locations from 2009 to 2017 in the Yuxi area and ecological data (weather and soil) corresponding to the different growth periods. These factors were analyzed to obtain a comprehensive index of the influential ecological factors and to establish an ecological prediction model of the chemical composition mechanisms of tobacco leaves. Using the ecological data from 2018, the content of main chemical components in the tobacco leaves was predicted and compared with the observed values. Data from 90 flue-cured tobacco samples were used to calculate the maximum information coefficient (MIC) to screen the input variables; this method ensures the integrity of the input parameters and is not limited to specific function types (e.g., a linear function) as long as there is a significant functional relationship between the ecological factors and chemical components. To overcome the shortcomings of the back-propagation (BP) neural network (i.e., it is easy to fall into local minima and slow convergence speed), the Grey Wolf optimizer was used in the modeling process to optimize the weights and thresholds of the neural network. To establish an intelligent algorithm for the tobacco leaf chemical composition ecological prediction model, the absolute error was used to intuitively show the difference between the simulated value of the BP neural network optimized by the Grey Wolf algorithm and that before optimization. The results showed that the prediction model of the mechanism algorithm could judge the degree of influence of the ecological factors on the tobacco chemical composition and indicate whether the influence was positive (promoting effect) or negative (adverse effect) by the size and the positive and negative values of the comprehensive index. The average R² value of the ecological prediction model of the mechanism algorithm was 0.29, the average root mean square error (RMSE) was 0.13, and only the RMSE of the reducing sugar was slightly greater than 0.2. These results indicated that the model understood the chemical composition characteristics of Yuxi flue-cured tobacco under particular ecological conditions in a given year. The absolute error of the ecologyical prediction model of the optimized intelligent algorithm was significantly smaller than that before optimization, indicating a better simulation effect for the optimized intelligent algorithm of the ecological prediction model. All R² values were greater than 0.95, and the R² values of the other prediction models (except for total nitrogen) were as high as 0.99. This suggested a very high degree of fit and that the model did well to explain the variability in the chemical composition; each RMSE was less than 0.1, and some values were less than 0.01, suggesting accurate prediction results.