A processor-implemented method comprising: training, via one or more hardware processors, an artificial intelligence enabled model with one or more sample datasets pertaining to: a plurality of materials to be used in an additive manufacturing, and a plurality of design information of a plurality of medical devices manufactured by additive manufacturing; receiving by the trained AI enabled model, via one or more hardware processors, inputs pertaining to a patient health record, the design information of a medical device, and at least one image of each of the plurality of materials to be used in the additive manufacturing of the medical device; auditing, via one or more hardware processors, the received design information of the medical device using the trained AI enabled model and a smart contract of a blockchain, wherein the smart contract comprises one or more regulation parameters to be followed in manufacturing of the medical device using the additive manufacturing; determining, via one or more hardware processors, one or more characteristics of each of the plurality of materials of the audited design of the medical device using the trained AI enabled model, wherein the determined one or more characteristics are updated in the smart contract of the blockchain; analyzing, via one or more hardware processors, design risks of the medical device based on the trained AI enabled model and a consensus is achieved among a plurality of stakeholders through the smart contract of the blockchain, wherein the analyzed medical device is tracked using one or more Internet of things (IoT) devices, wherein the trained AI enabled model analyzes design risks based on the audited design and one or more characteristics of plurality of materials used in the additive manufacturing of the medical device, and wherein the design risks include injury, uneven strength leading to breaking, potential non-performance of the medical device for intended purpose; and labelling, via one or more hardware processors, the analyzed medical device with a unique code based on one or more information received from the one or more internet of things (IoT) devices and the trained AI enabled model, wherein the unique code comprises one or more information of the medical device. training, via one or more hardware processors, an artificial intelligence enabled model with one or more sample datasets pertaining to: a plurality of materials to be used in an additive manufacturing, and a plurality of design information of a plurality of medical devices manufactured by additive manufacturing; a plurality of materials to be used in an additive manufacturing, and a plurality of design information of a plurality of medical devices manufactured by additive manufacturing; receiving by the trained AI enabled model, via one or more hardware processors, inputs pertaining to a patient health record, the design information of a medical device, and at least one image of each of the plurality of materials to be used in the additive manufacturing of the medical device; auditing, via one or more hardware processors, the received design information of the medical device using the trained AI enabled model and a smart contract of a blockchain, wherein the smart contract comprises one or more regulation parameters to be followed in manufacturing of the medical device using the additive manufacturing; determining, via one or more hardware processors, one or more characteristics of each of the plurality of materials of the audited design of the medical device using the trained AI enabled model, wherein the determined one or more characteristics are updated in the smart contract of the blockchain; analyzing, via one or more hardware processors, design risks of the medical device based on the trained AI enabled model and a consensus is achieved among a plurality of stakeholders through the smart contract of the blockchain, wherein the analyzed medical device is tracked using one or more Internet of things (IoT) devices, wherein the trained AI enabled model analyzes design risks based on the audited design and one or more characteristics of plurality of materials used in the additive manufacturing of the medical device, and wherein the design risks include injury, uneven strength leading to breaking, potential non-performance of the medical device for intended purpose; and labelling, via one or more hardware processors, the analyzed medical device with a unique code based on one or more information received from the one or more internet of things (IoT) devices and the trained AI enabled model, wherein the unique code comprises one or more information of the medical device.