  In the growing field of blockchain technology, smart contracts exist as
transformative digital agreements that execute transactions autonomously in
decentralised networks. However, these contracts face challenges in the form of
security vulnerabilities, posing significant financial and operational risks.
While traditional methods to detect and mitigate vulnerabilities in smart
contracts are limited due to a lack of comprehensiveness and effectiveness,
integrating advanced machine learning technologies presents an attractive
approach to increasing effective vulnerability countermeasures. We endeavour to
fill an important gap in the existing literature by conducting a rigorous
systematic review, exploring the intersection between machine learning and
smart contracts. Specifically, the study examines the potential of machine
learning techniques to improve the detection and mitigation of vulnerabilities
in smart contracts. We analysed 88 articles published between 2018 and 2023
from the following databases: IEEE, ACM, ScienceDirect, Scopus, and Google
Scholar. The findings reveal that classical machine learning techniques,
including KNN, RF, DT, XG-Boost, and SVM, outperform static tools in
vulnerability detection. Moreover, multi-model approaches integrating deep
learning and classical machine learning show significant improvements in
precision and recall, while hybrid models employing various techniques achieve
near-perfect performance in vulnerability detection accuracy.
  By integrating state-of-the-art solutions, this work synthesises current
methods, thoroughly investigates research gaps, and suggests directions for
future studies. The insights gathered from this study are intended to serve as
a seminal reference for academics, industry experts, and bodies interested in
leveraging machine learning to enhance smart contract security.
