Organizations face challenges in managing large, diverse datasets while ensuring efficient analytics and real-time decision-making. Traditional data warehouses and lakes offer benefits but struggle with integrating structured and unstructured data seamlessly. The Lakehouse architecture combines the reliability of warehouses with the flexibility of lakes, enabling advanced analytics. This study analyzes 38 papers from IEEE, ACM, Scopus, and Web of Science to examine Lakehouse implementations, focusing on functional zones, technologies, objectives, data sources, publication trends, and industry adoption. Using ATLAS.ti for qualitative coding and R for statistical analysis, findings show that data processing speed, cost efficiency, and scalability are key priorities. Apache Spark, Delta Lake, and Parquet dominate the ecosystem, supporting efficient data transformation and querying. Adoption is growing, especially in healthcare, energy, and telecommunications, but security, compliance, and data resiliency remain underexplored. As Lakehouse technologies evolve, this study highlights key trends, challenges, and future research opportunities in modern data management and large-scale data integration.