This narrative review article examines medication errors (MEs) and medication-related problems (MRPs), which affect medication safety for more than 50% of hospitalized patients and can lead to adverse outcomes. MRPs include preventable events that lead to incorrect medication use or harm in the hands of healthcare professionals and patients, harmful responses to a drug at typical dosages, and injuries related to drug use (ADEs). In recent decades, these adverse effects have decreased in prevalence, partially due to use of artificial intelligence (AI) and machine learning (ML) in hospitals. These models use patient data to analyze risk levels and consequently alert clinicians when the risk of ADEs is high. Additionally, natural language processing (NLP) AI technology analyzes unstructured data and identifies other potential ADEs. Beyond AI and ML, barcode labels and smart infusion pumps also improve medication safety. However, challenges remain in medication safety. Alert fatigue, inconsistent data quality, and limited testing for fairness can reduce the reliability of AI-driven tools. Additionally, high-risk medications, polypharmacy, and transitions of care continue to be common sources of MEs and MRPs. Long-term progress will depend on improved health technology, teamwork across healthcare roles, careful monitoring, and adapting safety practices to new models of care such as Hospital-at-Home.