This study examined the potential of artificial intelligence (AI) for enhancing system integration in pharmaceutical Quality Control (QC) operations, aiming to enhance efficiency, accuracy, and regulatory compliance. A mixed-methods approach was utilized, integrating quantitative survey analysis of QC and QA professionals with qualitative thematic coding of open-ended responses. Data from 84 industry participants were analyzed using descriptive statistics, binary coding, and frequency distribution via SPSS, facilitating the clear identification of AI adoption trends, operational advantages, and current obstacles. The results demonstrated substantial evidence of AI's impact on operational performance: 75% of participants indicated a decrease in deviations and errors, 66% noted enhancements in turnaround times, and over 60% recognized improvements in system integration and predictive analytics. Thematic insights emphasized significant enhancements, including expedited defect detection, automation of documentation, and enhanced audit preparation; however, obstacles such as elevated implementation costs, data quality requirements, and integration difficulties with legacy systems were identified. The results validate the hypothesis that AI-enabled systems markedly enhance quality control processes by reducing manual workloads, improving real-time monitoring, and strengthening compliance frameworks. The study concludes that although the integration of AI in pharmaceutical quality functions is progressing, success depends on overcoming infrastructural, training, and regulatory obstacles. Practical recommendations encompass the formulation of specific AI-specific Standard Operating Procedures, allocation of resources for workforce training, and implementation of incremental digital transformation strategies. This study provides empirical evidence regarding AI integration in pharmaceutical quality systems, establishing a basis for future research on expanding AI adoption in highly regulated industries.