One of the emerging technologies that is used in sports engineering is machine learning. Datasets and algorithms play an important role in achieving the desired results in the field of sports engineering. We have lot of data and it plays a major role in talent identification, training and coaching, umpiring and scoring, analysis and insight generation, injury management and recovery, fan engagement, e-sports and betting, etc. The data we acquire through all of these is being trained and tested using available machine learning algorithms. The automation using artificial intelligence will be in place and it is in development, which will add more value to that data and its visualization for the audience. Qualitative research on various findings and works carried out around the world in terms of sports data and machine learning is analyzed to know the future of machine learning in sports engineering with all types of machine learning: supervised, semi-supervised, unsupervised, and reinforcement learning. Machine learning leads to more progressive development in sports engineering, mainly in track and field, football, hockey and cricket, etc, and other applications like betting and e-sports are in demand for the future. Future developments on machine learning in sports engineering are discussed in this for further developments. By developing such solutions in the future, all sports enthusiasts will get benefits. Especially, athletes and coaches will benefit. Also, other aspects like fan engagement, injury, betting, manufacturing, and testing can also be achieved.