###### Abstract
A convolutional neural network machine-learning algorithm is considered to instantly estimate the quality of manufactured products using a statistical f1-score of the predictions [30]. In addition, to detect and monitor COVID-19 patients, a number of machine-learning algorithms, including recurrent neural networks, generative adversarial networks, and new ML-based applications, were recently addressed [31]. An advanced machine-learning algorithm called reinforcement learning, which learns the optimal control tasks through a trial and error process, was implemented for the lateral control of a nonlinear vehicle moving in a constrained lane [32]. This study aims to present a machine learning model to be used for the prediction of the number of departing foreign visitors from Turkiye by reasons for the next 10 years to gain a deeper understanding of their future behavior. The behavior of departing foreign visitors is considerably affected by various uncertainties, including the pandemic outbreaks, political turmoil, and global economic crises. Henceforth, the genetic algorithm used in this study is modified to reduce uncertainties and predict future trends by focusing on the dominant character of the data. As a result, the algorithm captures the key pattern of the training data instead of the instant character, so the unseen uncertainties can be avoided. Moreover, a positive definite polynomial basis function is constructed to create a certain, smooth, and higher-order parameter space. This ensures that the model can capture swift and fine changes in the estimates. To optimize the unknown model parameters with the uncertain and limited training data, a constrained genetic algorithm, a gradient-free meta-heuristic algorithm, is modified. This allows us to build a robust model with proportionally distributed unknown parameter magnitudes. Finally, the algorithm is free of pre-processing approaches, such as normalization and generalization. This causes extra difficulties in the training process of the machine learning algorithms, but it allows us to directly perform the future predictions in the real data magnitude space. Marketing is defined to be an \"applied-synthesis social science\" [33], and it is evolving from a descriptive to a predictive science [34]. Thus, the contributions of the study can be briefly summarized:  
1. It enhances the knowledge reserve in consumer behavior due to the robust prediction of the reasons for future foreign visitors' behavior with machine learning, as previous studies are mostly based on descriptive knowledge and past statistical data;
2. It develops a multi-dimensional machine-learning model trained effectively with limited uncertain data and can predict the number of departing foreign visitors by their reasons for the next 10 years;
3. The developed model can be trained with the data collected from different cities, regions and countries to predict the future arrival reasons of foreign visitors (consumers). This study can also be helpful for global and local firms in tourism, governmental agencies, and civil society organizations that engage in reducing the effects of COVID-19 on tourism and pursue sustainable development in tourism. This study can provide useful customer insights for marketers engaging in designing marketing strategies based on customer value (segmentation, targeting, differentiation, and positioning) and marketing mix.