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This work addresses the combination of machine learning (ML) and natural language processing (NLP) approaches to optimize the process of courting and RxNorm mapping inside Electronic Health Records (EHRs). Cohorting patients based on comparable traits or diseases is vital for clinical research, but it generally depends on time-consuming manual techniques and is prone to mistakes. Similarly, mapping pharmaceutical names to standardized codes such as RxNorm promotes interoperability and data analysis but may be challenging owing to variances in how drugs are reported. Leveraging ML and NLP may automate and optimize these procedures, leading to more efficient cohort identification and precise medication mapping. We offer a thorough technique for integrating ML and NLP algorithms in EHR systems, including data preparation, feature engineering, model training, and assessment. Through testing and analysis, we show the usefulness of our technique in enhancing cohorting accuracy and RxNorm mapping precision. The findings underline the promise of ML and NLP in revolutionizing EHR data management, leading to improved patient care and simplified research procedures.