Artificial intelligence (AI) is rapidly revolutionizing the pharmaceutical sciences and clinical pharmacy practice by addressing ever-increasing data burdens and optimizing personalized patient care. This manuscript comprehensively explores the integration of AI across six critical clinical domains: medication safety and pharmacovigilance, pharmacokinetics and dose optimization, drug discovery and repurposing, therapeutic drug monitoring, patient adherence support, and pharmacy automation. While AI techniques, such as machine learning and deep learning, offer tremendous potential to enhance therapeutic efficacy, minimize errors, and streamline healthcare workflows, their clinical implementation remains highly complex. The paper underscores the absolute necessity of rigorous evaluation methods including experimental validation and randomized controlled trials to ensure system accuracy and generalizability across populations. Furthermore, realizing the full value of AI requires navigating significant operational and ethical challenges. These notably include ensuring data quality and privacy, mitigating algorithmic bias, and addressing the critical need for explainable AI (XAI) to foster trust among healthcare professionals. Shifting professional liabilities and the demand for updated regulatory frameworks are also thoroughly examined. Ultimately, proactive stakeholder engagement and standardized interoperability practices are essential to successfully harness AI's transformative capacity, ensuring it safely advances the modern pharmacy profession.