The landscape of drug discovery is witnessing a paradigm shift with the integration of artificial intelligence (AI) and machine learning (ML) models into traditional approaches. This paper explores the synergy between AI/ML and high-throughput screening (HTS) techniques to expedite the identification of novel therapeutic compounds. By leveraging AI/ML algorithms, researchers can analyze vast amounts of biological and chemical data, uncover hidden patterns, and predict compound activities with remarkable accuracy. HTS, on the other hand, enables the rapid testing of thousands to millions of compounds, allowing for comprehensive screening of chemical libraries. This paper provides an overview of the principles and methodologies of HTS, highlighting its advantages and limitations. It also delves into the various AI/ML techniques employed in drug discovery, including deep learning, reinforcement learning, and generative adversarial networks, elucidating their roles in target identification, compound optimization, and toxicity prediction. Moreover, the integration of AI/ML with HTS is examined, elucidating the rationale behind this fusion and the methods utilized to achieve it. Case studies are presented to showcase successful applications of AI/ML-HTS integration in accelerating drug discovery processes. Looking ahead, the paper discusses future perspectives and emerging trends in AI-driven drug discovery, such as the integration of multi-omics data, personalized medicine approaches, and the ethical considerations surrounding AI implementation in healthcare.