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  • This paper integrates the principles of Maharishi including Transcendental Meditation and collective consciousness with a learning model in the Social Internet of Things (SIoT). SIoT is the fusion of social networks and connected devices into an environment where machine learning is used to produce completely new systems with the capabilities to evolve or change. Although machine learning methods have transformed many fields, introducing Maharishi’s holistic and consciousness-oriented methods offers a distinct possibility to create more natural, dynamically responding and sustainable AI systems. This research proposes a framework that connects human consciousness and machine intelligence, leveraging Maharishi’s principles to then influence SIoT learning models toward technical proficiency, ethical awareness, and social responsibility. The paper starts with a summary of Maharishi’s teachings and shows their applicability in the context of contemporary technological progress. Next, the structure and operation of SIoT are described, with an emphasis on the way learning algorithms will work across interconnected devices. The potential of using Maharishi’s consciousness-based principles in the scholarship of machine learning is probed in key sections through the lens of cognition models, ethical decision-making, and collective intelligence in machine networks. Case studies and practical applications of this integration toward improving system resilience, decision-making, and human–machine interactions are provided through this research. The final part of this paper addresses the challenges and opportunities of integrating Eastern philosophy with Western technological paradigms and suggests future avenues for research in this interdisciplinary field. Maharishi’s principles of integration mean that AI can evolve into something transformative on both counts—creating more efficient and innovative ways, while at the same time increasingly aligned with human values and societal wellbeing.

Last update from database: 3/30/26, 1:00 AM (UTC)