The study and application of mode choice models have increased, having a growing number of publications in recent years with a trend of applying machine learning methods. Despite this trend and the relevance of these mode choice decisions in transportation planning, to the best of our knowledge, there is no formal literature review exercise that helps future research to identify decisions, data collection approaches, models, and methodologies that best apply to their particular contexts. Thus, the objective of this paper is to determine the current methods and data sources used to model mode choice for transportation in urban areas. To achieve this, we perform a systematic literature review to compare the different methods, techniques, data sources, and metrics used to model mode choice behavior and evaluate their performance. In addition to the characterization of the studies, we document the relevance of performing a survey design stage, ensuring the collection of required variables. Furthermore, we report the tendency to complement econometric classic models with machine learning models.