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This paper focuses on modeling transportation mode choice for commuting to work and school. The study employs a Multinomial Logit (MNL) model to examine how individuals choose their modes of transportation for work and school trips in Hawassa city. Additionally, the study aims to predict both the current and future distribution of transportation modes. To construct the model, surveys were distributed across the city's seven sub-cities, involving inspections of workplace and school travel. Primary data were collected through site visits to key transportation hubs, and travel cost data were obtained from the city's Road and Transportation Bureau. The data used for the MNL model describe the travel behaviors of employees and students, and these behaviors are integrated into the statistical analysis to formulate utility functions. The choice of transportation mode for a trip is treated as the dependent variable, while independent variables include factors like out-of-vehicle travel time, in-vehicle travel time, and travel cost. The model's validity and accuracy were assessed by examining the direction of parameter signs and comparing them to the fundamental properties of the MNL model. The study revealed that factors such as average monthly income, in-vehicle travel time, out-of-vehicle travel time, total travel cost, and comfort during the journey significantly influence individuals' choices of transportation modes. The results from the travel behavior forecast, which examines how employees choose their transportation modes, highlight the pressing need for implementing effective policy measures to incentivize the adoption of more sustainable transportation modes and promote a modal shift. Without such measures, employees are likely to increasingly favor motorcycles as their preferred mode of transportation, potentially exacerbating issues related to fuel consumption and congestion. It's evident that students tend to favor public transportation over motorcycles when selecting their mode of travel. The findings of this study offer valuable insights to decision-makers and transportation planners, shedding light on the critical factors influencing travel patterns, as well as providing estimates of existing and future market shares. These findings can serve as a foundation for crafting targeted policy adjustments to encourage sustainable transportation choices in a comprehensive manner.