Leveraging multiprocessing for enhanced parallelism in big data engineering for geotechnical issues can significantly improve the efficiency and speed of data processing. Geotechnical data often involves large datasets, complex simulations, and extensive computations. The integration of Big Data Technology for improved geotechnical solutions in energy, infrastructure, and disaster management contexts. Focusing on Apache Spark and Cloud Architecture, the study aims to enhance site selection, foundation design, and risk mitigation through large-scale data analytics. Methodologically, a thorough literature review, case study analysis, and exploration of diverse applications form the basis of the study. Results underscore the transformative impact of Big Data on decision-making, predictive modeling, and disaster resilience. The article concludes by addressing challenges, showcasing successful applications, and outlining future directions, emphasizing the pivotal role of Big Data Engineering through enhanced parallelism in advancing geotechnical practices for a sustainable and resilient future.