A mode-choice model is typically used to analyze and predict a traveler's choice of mode of transportation when making a trip. It deals with traveler's choice, behavior, and preference of choosing the mode of travel among the available modes to reach a destination. These models are complex by nature because of the many factors involved, including factors related to the characteristics of the trip, the trip-maker, and the transportation mode itself. The main goal of this thesis is to develop an Artificial Neural Network (ANN) Mode-Choice Model for Sharjah University City (SUC) in the UAE. A travel survey was conducted in 2018/2019 to collect travel preferences among travelers onto SUC. Over 4,200 respondents indicated their preferences as they traveled to the campus. A preliminary statistical analysis of the collected data was performed to identify the factors contributing to the models as well as the adequacy of the sample size to represent SUC's population. Statistics show that the sample almost equally represents both genders. The majority of the participants were expatriates with 74%. Moreover, students make the majority of the sample size with 85% while the rest were faculty, staff, and visitors. Also, 84% of participants mentioned that their household owns at least 2 cars, which indicates a good level of income. Regarding car-ownership, two-thirds of the respondents own a private car. For car-sharing, only 13% of the sample share the ride with someone. For mode-choice distribution, 61% of travelers use private cars to commute, while only 18% travelers reach their campus via walking. Unfortunately, only few commuters are taking private bus or Taxi with 3% and 1%, respectively.