Text-to-SQL systems have gained prominence for enabling natural language interfaces to databases, allowing users to query data without needing expertise in structured query languages. Despite their potential, existing research remains predominantly focused on English, with limited attention given to non-English languages. This paper explores recent efforts aimed at bridging this gap by examining the development of Text-to-SQL systems for other languages. We review both translated and original datasets, analyze approaches used to adapt existing models to other languages and discuss the challenges arising from linguistic diversity such as structural differences and the lack of annotated training data. Additionally, we propose directions for future research. By synthesizing current limitations and highlighting promising areas for advancement, we outline a roadmap for building more inclusive and robust Text-to-SQL systems in other languages.