Concrete forms the backbone of modern infrastructure, but small cracks that develop with time can significantly diminish its durability and longevity. Bioconcrete embeds beneficial microbes such as Bacillus subtilis or Sporosarcina pasteurii, offering an innovative solution through in situ precipitating calcium carbonate into the cracks. However, effectiveness depends on many factors regarding bacterial activity, nutrient supply, crack size, and environmental conditions, thereby making the performance of bio-concrete difficult to predict. In recent times, machine learning techniques have shown great promise in helping scientists and engineers predict the efficiency of the healing in bio-concrete and optimizing its material design. This review describes how ML models, such as neural networks, support vector machines, and ensemble learning, have been used for the prediction of crack closure, strength recovery, and long-term durability of bio-concrete. It also discusses some important challenges, such as limited experimental datasets, variability in microbial behavior, and environmental influences. At the same time, hybrid models, real-time monitoring integration, and optimization frameworks can make bio-concrete more reliable and easier to deploy on a large scale. By bringing together the latest developments, this article provides a clear prospect for anybody interested in applying machine learning to improve the performance, sustainability, and resilience of bio-concrete in various infrastructure projects