Despite over a decade of research, gender bias in machine translation remains a complex challenge with no simple fix. A review of 133 studies highlights both promising trends and persistent gaps, such as narrow technical focus, English-centric approaches, and a lack of real-world grounding. These findings call for future research to move beyond past limitations and to be more user-centric, culturally diverse, and personalized.