This study aims to discuss the concepts, characteristics, and principles (models) of meta-learning, as a strategic model for machine learning, which might supplement humanistic experiential learning theory. To address this issue, this paper suggested three kernel research questions: i) What are the basic principles and traits of experiential learning and how are the learning environments (spaces) and modalities changing in the technology-oriented society?, ii) What are the notions and basic structures of meta-learning and what principles and models can be adopted as the meta-learning practices?, iii) What is the actual case of meta-learning employed in a technology-based learning milieu and what are the seminal principles and necessary tools for it? This paper’s biggest arguments are as follow: First, experiential learning has been solely interested in contact, personal, and aspects of contextual experiences for cognitive learning, while meta-learning has special attention on non-contact, indirect, beyond-context, and a mediated learning. Second, since meta-learning is especially useful for intuitive and technology-based learning situations, it helps much the learning practices of technology-friendly learners. Third, the meta-learning principles (models) may include meta-reinforcement learning, distributed learning, federated learning, and self-adaptive learning. Fourth, meta-learning has been actualized in the computer-supported collaborative learning, which highlights the individual, mediated, and self-adaptive learning that utilizes technological tools diversely in a digital learning platform. This paper’s suggestive arguments on meta-learning may promote active discussions in academic fields about the link between humanistic experiential learning and machine learning.