Techniques for advanced computation have become essential because of modern data growth requirements to process large datasets effectively. The strength of classical computing systems meets its limit when dealing with both extensive data quantity and high analytical complexity. This chapter demonstrates how quantum algorithms serve as solutions to resolve current problems. Quantum mechanics principles such as superposition, entanglement, and inference enable quantum algorithms to conduct data clustering combinations at incredible speeds and with high efficiency for pattern identification, optimization, and machine learning operations. A combination of quantum neural networks with variational models and accelerated Markov decision processes and generative adversarial networks functions as a hybrid approach which enhances large data analytics together with classification, optimization and high-dimensional data synthesis capabilities. This chapter also analyzed both existing barriers and future perspectives of quantum computing for this field yet also provide information about hybrid quantum/classical systems which could act as a bridge until quantum computers fully scale up. This chapter delivers an extensive review of quantum algorithm transformations within big data science which guides both data science and quantum computing professionals.