<![CDATA[Inclusive education aims to provide an equitable and supportive learning environment for all students, including those with special needs. In recent years, machine learning (ML) has become one of the technologies offering innovative solutions to enhance the quality of inclusive education. This study aims to review the role of machine learning in inclusive education through a systematic analysis of literature published in the last five years. The Systematic Literature Review (SLR) methodology is applied to identify, evaluate, and synthesize findings from various related studies. The results of the review show that ML algorithms such as decision trees, support vector machines, and neural networks are widely used to detect student needs, personalize learning, and improve accessibility and learning outcomes. However, challenges such as algorithmic bias, lack of quality data, and implementation barriers still require further attention. This review provides insights into the opportunities and challenges of using machine learning to support inclusive education and offers recommendations for future research and development.]]>
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