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Despite tremendous promises, Additive Manufacturing (AM) still faces many challenges to matching the standards of conventional manufacturing and reaching its full potential in bridging the gap between the consumer, the designer, and the production. Driven by these challenges, the research and industry communities are focused on not only making 3D printing machines work better either by designing robust control strategies or by designing advanced monitoring methodologies, but also on developing new ways to enable enhanced human interactions in a feedback loop. In this thesis, we design an immersive human-centric cyber-physical system, named I-nteract, to provide a framework to develop intuitive and user-friendly interfaces that enable personal fabrication for non-technical users, streamline the AM process by allowing real-time testing of the designed 3D models, and provide effective means of monitoring the AM process to improve the build quality of the product. I-nteract is a Visio-Haptic Mixed Reality (VHMR) system that enables real-time processing of human-centered spatio-temporal data acquired by vision (HoloLens) sensors and wearable sensors (position sensors for hand & fingers tracking) to provide visual augmented reality feedback (via HoloLens) and force feedback (via haptic gloves) to enable human interaction with physical and virtual world simultaneously. We demonstrated the efficacy of our system by implementing novel practical applications to improve the AM workflow. We developed a novel scan-based method for the real-time monitoring of AM processes. With the incorporation of haptics within the MR system, we demonstrated how I-nteract allows real-time interactions with both digital and physical (deformable/non-deformable) objects simultaneously to streamline the AM process by enabling virtual testing phase prior to the manufacturing phase. We proposed a novel 3D scanning method and implemented this novel strategy to design a customised orthopaedic cast for a human forearm. We also implemented interactions with the deformable objects so that the user can capture the elasticity along with the shape of a physical object to generate and simulate its digital twin. Furthermore, we introduced generative functionalities in the design phase of the AM workflow by using Constructive Solid Geometry (CSG) and integrating Deep Learning (DL) within the system to automate the parts of the design process that require expert knowledge. We tested the system with two types of generative Deep Neural Network (DNN)s to design customised 3D models of chairs and tables from their respective single-view 2D images captured via HoloLens, and by resizing the 3D models using hands in an MR environment with respect to the design constraints imposed by the physical workspace. Finally, in the context of improving the AM process to achieving stringent precision requirements by designing robust control strategies for the widespread adoption of AM technologies in the industrial sector, we proposed modal approximation based control strategies for the physical processes modelled by a reaction-diffusion equation. Heat flow is an important parameter of the AM process to achieve the high quality of the product. The systems where heat is produced and diffuses away from the heat production site are described by reaction-diffusion equations. First, we proposed state feedback control for a reaction-diffusion equation with a state delay in the reaction term. Then, to enhance the pragmatic feasibility of our proposed control design approach for practical application, we designed a finite-dimensional observer-based control strategy for the output feedback stabilization and setpoint regulation of a reaction-diffusion equation cascaded with an Ordinary Differential Equation (ODE). We showed that the control design strategies achieve both the exponential stabilization as well as the setpoint regulation of both the systems.