Cell-free massive MIMO communication systems is a promising technology that uses access-points(APs) deployed throughout the coverage area instead of usual cellular systems with centralized BS to serve multiple users simultaneously. By exploiting the large number of antennas and adopting advanced signal processing techniques, cell-free massive MIMO can mitigate inter-user interference and enhance the overall system performance. Optimal power allocation plays a crucial role in maximizing the spectral and energy effciency of wireless networks. By intelligently allocating transmit power to different users, a balance between maximizing the system throughput and minimizing the total energy consumption can be achieved. In addition, user-centric clustering(UCC) is also a key technique to improve the performance of cell-free massive MIMO systems. This technique aims to pair user equipments (UEs) with appropriate APs to facilitate effcient resource allocation and interference management. In this thesis, cell-free mMIMO communication system is investigated through user-centric clustering and power allocation. The power allocation optimization problem is formulated to maximize energy effciency of cell-free mMIMO systems and solved by using interior-point algorithm. User-centric clustering algorithm is proposed by disabling the nonmaster APs that are serving only one user. This additional feature aims to reduce total power consumption of the system without sacrifcing the advantages of the cell-free mMIMO communication systems. Additionally, we propose a machine learning(ML) approach to reduce the computation time required for power allocation optimization. Through extensive simulations, we demonstrate the effectiveness of the proposed algorithms in achieving signifcant gains in spectral and energy effciency in cell-free massive MIMO systems. The results highlight the importance of optimal power allocation and user-centric clustering to design an effcient cell-free mMIMO systems through machine learning approach.