In the modern era, the variable cultural background, learning styles, and cognitive abilities of the learners require different adaptive technologies. This chapter explores the different transformative roles of Machine Learning (ML) to address the key challenges in outcome-based curriculum design and teaching methodologies. The long-established methods of learning without adaptable individual learning trails often lead to inefficient evaluation. The need for a personalized, data-driven, outcome-based educational system and equitable access to quality education is the motivation behind this research. This chapter demonstrates how ML enables competency-based curriculum development, automated rubric-based assessments, and dynamic feedback analysis, helping educators create more effective teaching strategies. Using Natural Language Processing (NLP) and sentiment analysis, ML can evaluate student interactions, assess comprehension levels, and gauge emotional engagement, and also helps the mentors for real-time refinement of pedagogical approaches. Results in recent studies prove that there is progress in deeper insights into students, optimization of educational assessments, and curriculum alignments. These findings also emphasise the ethical considerations in intelligent systems. Future research should explore AI-driven personalization to further improve adaptive learning environments.