5G D2D Communication promises improvements in energy and spectral efficiency, overall system capacity, and higher data rates. However, to achieve optimum results, it is essential to select wisely the Transmission mode of the D2D Device to form clusters in the most advantageous positions in terms of Sum Rate and Power Consumption. Towards this end, this paper investigates the use of Distributed Artificial Intelligence (DAI) and innovative D2D, Machine Learning (ML) approaches (i.e., DAIS, FuzzyART, DBSCAN and MEC) to achieve satisfactory results in terms of Spectral Efficiency (SE), Power Consumption (PC) and execution time, with the creation of clusters and back-hauling links in D2D network under existing Base Station. Additionally, this paper focuses on a small number of Devices (i.e., =5 for clustering, >=50 for backhauling) as lower limits.