Pharmacovigilance is transforming at warp speed in response to big data and advanced analytical techniques. This paper will provide an overview of where pharmacovigilance currently stands by focusing on integrating artificial intelligence (AI), machine learning (ML) and real-world data (RWD) in order to improve drug safety monitoring. These new methods are increasingly supplementing traditional ones which serve as their base. The purpose of this survey is to assess how effective they are, point out the major challenges standing in their way as well as offer recommendations for future research. In conclusion, although AI and ML could prove helpful especially with handling large volume and complexity of datasets, there is a need for tackling data quality, integration issues and regulatory acceptance concerns first. Standardized methodologies should be worked out and collaboration among all stakeholders encouraged so as to maximize the pharmacovigilance benefits that can come from these technologies.