Abstract: Antibiotic resistance is one of the most serious global health challenges, where bacteria evolve mechanisms to resist the effects of antibiotics. This leads to treatment failure, prolonged illness, increased healthcare costs, and higher mortality rates. Traditional laboratory-based methods for detecting antibiotic resistance require significant time and manual interpretation. This project proposes an Artificial Intelligence (AI)-based system that analyzes patient laboratory data such as bacterial type, antibiotic tested, and Minimum Inhibitory Concentration (MIC) values to predict whether the bacteria are resistant or sensitive to specific antibiotics. Machine Learning algorithms are used to automate the detection process, enabling faster and more accurate clinical decision-making. The system helps healthcare professionals select appropriate antibiotics and reduces misuse, thereby contributing to better patient outcomes and combating antimicrobial resistance.

Keywords: Antibiotic Resistance, Machine Learning, Healthcare AI, MIC Analysis, Clinical Decision Support.

Downloads: | DOI: 10.17148/IARJSET.2026.13372