Quantum Computing emerged from the pioneering ideas of Paul Benioff and Richard Feynman in the early 1980s. However, due to the technological limitations of the time, constructing a stable Quantum Computer remained infeasible, leaving Quantum Computing to theoretical and algorithmic realms. Algorithms such as Shor’s and Grover’s have since demonstrated the superiority of Quantum Models over Classical Models. In recent years, technological advancements have equipped us with the means to develop stable and capable Quantum Computing Systems, exemplified by entities like Google (Quantum Supremacy), Microsoft, IBM Q, Rigetti, Honeywell, and IonQ. Many of these systems are now accessible to individual users, much like other IAAS offerings. This work introduces the logic of Quantum Computing and delves into fundamental Quantum Algorithms to elucidate the programming patterns employed in utilizing Quantum Hardware. Subsequently, we examine several Quantum Machine Learning Algorithms. Specifically, we develop a hybrid Quantum-Classical algorithm for k-means clustering and theoretically demonstrate its supremacy over classical algorithms in terms of execution time. We validate its efficiency in a Quantum Simulation Environment. Finally, we test the algorithm on real Quantum Hardware provided by IBMQ, drawing conclusions about the stability and efficiency of contemporary Quantum Computing Systems.