Matching markets, where agents are assigned to one another based on preferences and constraints, are fundamental in various AI-driven applications such as school choice, content matching, and recommender systems. A key challenge in these markets is understanding preference data, as the interpretability of algorithmic solutions hinges on accurately capturing and explaining preferences. We introduce MatchXplain, a platform that integrates preference explanation with a robust matching engine. MatchXplain offers a layered approach for explaining preferences, computing diverse matching solutions, and providing interactive visualizations to enhance user understanding. By bridging algorithmic decision-making with explainability, MatchXplain improves transparency and trust in algorithmic matching markets.