Increasing wireless communication requirements of data rates, capacity and coverage, and evolution and maturation of wireless equipment prompt wireless communication research insight concentrating on millimeter wave (mm-wave) frequency. However, high reflection coefficients and high path loss cause large shadow areas (e.g. behind the buildings) and poor coverage, leading to constraints on wireless connectivity and effectiveness of wireless communication. Intelligent Reflecting Surface (IRS) is a revolutionizing technology in 6th-generation mobile networks (6G), which achieves extended coverage with reducing construction and electricity costs via its characteristics of passive beamforming and proper deployment, and auxiliary of Ray Tracing (RT) facilitates obtention and analysis of channel state information (CSI) at different locations. With the objective of developing a flexible RT tool and a novel methodology for optimal IRS deployment to maximize coverage in non-line-of-sight (NLOS) areas from the BS, a new RT simulation model is built in this thesis project in accordance with measured data, leading to improved reliability and accuracy of 5.3\% maximum error in path loss. And a novel, but preliminary, weight graph methodology is proposed for tackling the IRS deployment problem for coverage extension in NLOS areas quasi-optimally. To integrate the IRS with RT simulation, a first-time comparison between metal reflectors and IRS under realistic EM effects is exploited. The obtained simulation results unveil that deploying IRS with the proposed weight graph methodology facilitates wireless coverage improvement, and the coverage probability increased from 0% to 96.23% with a threshold of -75 dBm under 28 GHz in a selected Region of Interest (RoI).