1. A blockchain configured distributed architecture-based system in a communication network, said system comprising: a memory circuit communicatively connected to said communication network that stores a plurality of digital profiles associated with a plurality of crowdsourced experts, and further stores a plurality of segmented digital profiles associated with each of said digital profiles, wherein said segmented digital profiles and digital profiles are created based on a plurality of sources distributed and electronically linked across said communication network; a processor coupled with the memory circuit to execute instructions for evaluating an expert, the instructions comprising: a credentialing engine that allows a plurality of crowdsourced respondents to respond to said segmented digital profiles associated with each of said plurality of experts and credential said plurality of experts and determine crowdsourced credentialed expertise, wherein the credentialing of each of said segmented digital profiles associated with an expert of said plurality of experts contribute to credentialing of a digital profile of said expert upon collation of said credentialed segmented digital profiles, and wherein said segmented digital profiles associated with said experts are credentialed from a plurality of respondents using a computerized crowdsourcing index, wherein said computerized crowdsourcing index is indicative of number of respondents credentialing an expert and dynamically increases with an increase in said number of respondents; an expert scoring module to: determine a set of attributes for said experts, said set of attributes including one or more of said crowdsourced credentialed expertise determined based on said credentialing of said segmented digital profiles of said experts by said respondents, reputation of said experts indicative of a trust of a relevant community on said experts, and officiality indicative of a position or a designation of said experts in a relevant job, wherein each of said attributes are assigned varying computer-calculated weights; and determine an aggregate score of an expert based on said one or more attributes in association with the assigned weights; an electronic document scoring engine to receive and process comments and document ratings for an electronic document by said crowdsourced experts, wherein said crowdsourced experts have an aggregate score greater than a defined threshold, the document scoring engine comprising: a natural language processing-based (NLP-based) analysis engine to process textual information-based reviews and comments generated as part of textual review of said electronic document by said crowdsourced experts; a visual scoring engine for processing visual and non-textual feedback and reviews by the crowdsourced experts, wherein the visual scoring engine comprises: an eye tracks processor controlled by a special purpose microprocessor to receive eye track inputs from respective eye tracking systems associated with computing devices of said crowdsourced experts and process said eye track inputs to associate a review score based on predefined eye track patterns; and a micro expressions processor to receive data indicative of micro facial expressions extracted by respective micro expressions sensors associated with said computing devices of said crowdsourced experts, wherein said micro expressions processor comprises an image processing circuitry and an associated memory to interpret said micro facial expressions and compare them with predefined facial patterns to associate a review score based on said extracted micro facial expressions, wherein, the document scoring engine further configured to: associate an aggregate score to said electronic document based on aggregation of individual textual and visual review scores obtained by processing of said textual and said visual reviews by said crowdsourced experts who review the document; and display on a graphical user interface device, an output indicative of an aggregate score of the document reviewed by said crowdsourced experts along with information about who reviewed and how many times reviewed the document; an expert identity validation device to verify identities of the crowdsourced experts during or prior to review, wherein said expert identity validation device comprises: a device patterns assessment device to receive and process device information extracted by respective agent devices associated with said computing devices of said crowdsourced experts and verify the extracted device information with predefined device information for the respective crowdsourced experts; a network patterns assessment device to receive and process network information extracted by said respective agent devices associated with said computing devices of said crowdsourced experts and verify the extracted network information with predefined network information of the respective crowdsourced experts; a geo-spatial mapping device to perform geo-tagging of the crowdsourced experts and the documents reviewed by said crowdsourced experts and compare the geo-tags with pre-stored geo-spatial information about the experts for processing validation, wherein the geo-tagging is performed based on geo-spatial information received from a global positioning system (GPS)-based device; and a facial expression validation device to receive and process facial expressions received from respective facial expression sensors associated with said computing devices of said crowdsourced experts and verify identity in accordance with respective predefined facial patterns of said crowdsourced experts, wherein the facial expression validation device comprises a digital acquisition unit and multichannel amplifiers for pre-processing and amplification of signals transmitted by said facial expression sensors.