Many individuals who are in need of mobility assistance do not have access to the proper wheelchair for their type of mobility disability. There is growing research towards creating smart wheelchairs using a variety of methods, such as biopotential signals or eye tracking for input and LiDAR, ultrasonic sensors, or using a camera to create a map or track position. There have been other methods, such as voice control, sip and puff, and hand gestures, but there are disadvantages of these that can limit their usefulness. Smart wheelchairs should account for collisions, but also emphasize the safety and comfort of the user. In this paper, we review and classify state-of-the-art research in smart wheelchairs. Many machine learning models are used for various parts of wheelchairs, from mapping and signal processing to input classification. Smart wheelchairs rely on various hardware devices, such as eye trackers, electrode caps, EMG armbands, RPLidar, RGB-cameras, and ultrasonic sensors. Some hybrid models use a combination of methods to account for some of their limitations. Some research has leaned towards training games to help teach users. Future work should include improvement of classification methods for various input signals and improvement on the accessibility of the technology.