The world has recently been reminded of how important and disruptive a disease can be as COVID-19 becomes endemic. Nevertheless, there is another health crisis looming in the corner if a solution is not found as soon as possible-antimicrobial resistance (AMR). AMR is projected to claim 10 million lives per year if no solution is proffered by 2050 and is estimated to result in a loss of over $US 3 trillion annually by 2030. Artificial Intelligence (AI) particularly Machine Learning (ML) and Deep Learning (DL) in the last decade has proven useful in many fields including health care. This work intends to explore the different ways by which AI can be applied in resolving the antibiotic crisis including finding novel antibiotics from the vast chemical space, analysing medical images to aid rapid diagnosis of bacterial disease to prevent antibiotic misuse and finding patterns in genetic sequences of multi-drug resistance bacteria to enable how to overcome them. Various DL techniques from Discriminative models such as Convoluted Neural Networks (CNN) to generative models such as autoencoders will be explored on how AI will contribute to solving the antimicrobial resistance crisis.