Toxicity, defined as the potential harm a substance can cause to living organisms, requires the implementation of stringent regulatory standards to ensure public safety. These standards involve comprehensive testing frameworks, including hazard identification, dose-response evaluation, exposure assessment, and risk characterization. In drug discovery and development, these processes are often complex, time-consuming, and also resource-intensive. Toxicity-related failures in the later stages of drug development can lead to substantial financial losses, underscoring the need for reliable toxicity prediction during the early discovery phases. The advent of computational approaches has accelerated a shift toward modeling, virtual screening, and, notably, artificial intelligence (AI) to identify potential toxicities earlier in the pipeline. Ongoing advances in databases, algorithms, and computational power have further expanded AI's role in pharmaceutical research. Today, AI models are capable of predicting wide range of toxicity endpoints, such as hepatotoxicity, cardiotoxicity, nephrotoxicity, neurotoxicity, and genotoxicity, based on diverse molecular representations ranging from traditional descriptors to graph-based methods. This review provides an in-depth examination of AI-driven toxicity prediction, emphasizing its transformative impact on drug discovery and its growing importance in improving safety assessments.