the entire internet of things (iot) ecosystem is directing towards a high volume of diverse applications. from smart healthcare to smart cities, every ubiquitous digital sector provisions automation for an immersive experience. augmented/virtual reality, remote surgery, and autonomous driving expect high data rates and ultra-low latency. the network function virtualization (nfv) based iot infrastructure of decoupling software services from proprietary devices has been extremely popular due to cutting back significant deployment and maintenance expenditure in the telecommunication industry. another substantially highlighted technological trend for delaysensitive iot applications has emerged as multi-access edge computing (mec). mec brings nfv to the network edge (in closer proximity to users) for faster computation. among the massive pool of iot services in nfv context, the urgency for efficient edge service orchestration is constantly growing. the emerging challenges are addressed as collaborative optimization of resource utilities and ensuring quality-ofservice (qos) with prompt orchestration in dynamic, congested, and resource-hungry iot networks. traditional mathematical programming models are np-hard, hence inappropriate for time-sensitive iot environments. in this thesis, we promote the need to go beyond the realms and leverage artificial intelligence (ai) based decision-makers for “smart” service management. we offer different methods of integrating supervised and reinforcement learning techniques to support future-generation wireless network optimization problems. due to the combinatorial explosion of some service orchestration problems, supervised learning is more superior to reinforcement learning performance-wise. unfortunately, open access and standardized datasets for this research area are still in their infancy. thus, we utilize the optimal results retrieved by integer linear programming (ilp) for building labeled datasets to train supervised models (e.g., artificial neural networks, convolutional neural networks). furthermore, we find that ensemble models are better than complex single networks for control layer intelligent service orchestration. contrarily, we employ deep q-learning (dql) for heavily constrained service function chaining optimization. we carefully address key performance indicators (e.g., optimality gap, service time, relocation and communication costs, resource utilization, scalability intelligence) to evaluate the viability of prospective orchestration schemes. we envision that ai-enabled network management can be regarded as a pioneering tread to scale down massive iot resource fabrication costs, upgrade profit margin for providers, and sustain qos mutually