Intelligent Tutoring Systems (ITS) represent a pivotal frontier in personalized educational technology, leveraging advanced deep learning techniques to enhance skills acquisition. This study conducts a comprehensive analysis of key pedagogical parameters, prior knowledge, learning styles, feedback mechanisms, and knowledge transfer and evaluates their synergy with deep learning models, including Neural Networks, Transformer-based architectures, Recommender Systems, and Predictive Models. Through a systematic evaluation of learner characteristics and pedagogical design considerations to enhance skills acquisition. Our comparative analysis evaluates these models across accuracy, efficiency, scalability, and educational applicability, incorporating both qualitative insights and quantitative metrics. Our findings reveal that Transformer-based models outperform traditional ITS approaches in adaptive learning, while Recommender Systems significantly enhance personalized content delivery. Additionally, hybrid deep learning techniques improve knowledge transfer efficiency, leading to more tailored learning interventions. However, challenges remain in real-time adaptability, computational efficiency, and learner diversity, necessitating future refinements. By addressing gaps in real-time adaptability and learner diversity, we pave the way for next-generation educational technologies that enhance engagement and skill development across diverse educational contexts.