Cloud Computing Adoption and Big Data Engineering can work on predictive analytics and applications of intelligence (AI) as shown in Figure 1.0, which emphasize important advantages for companies by building models, or through machine learning, deep learning, or computer vision (Chaudhari et al., 2021) Thus, it has been observed that BDE is the driver of CCA (Hashim et al., 2015) (Liu, 2013), (Hemalatha et al., 2016), (Kong et al., 2017), (Chowdhury, 2018), and (Mirkovic, 2021) In order to examine this Claimed new model will be created that integration of the model related to BDE and other CCA variables that generated relative adoption of Technology Acceptance Model (TAM) and Technology Organization Environment (TOE) which will be verified through the survey and collection of data from UAE by using binary logistic regression for data analysis. Since developing technologies have strong ties to CCA and BDE, CCA has occurred in both hypothetical and business settings. Big Data Engineering is driving CCA among large organizations. Cloud computing must transform from weak technology to complex business solutions if it is to increase CCA. It was important to examine the technical advancement and changes in the current business landscape in order to recognize the effect of BDE on CCA, and the wider implications of BDE and CCA on organizations. Business organizations must produce advanced results at every level of their organizations to remain relevant in the BDE. An impact model for CCA was developed based on BDE variables, along with variables from two widely used technology adoption theories: technology acceptance model (TAM) and technology organization-environment (TOE). CCA was extended by the addition of BDE-related variables. There were six independent variables: usefulness, ease of use, security effectiveness, cost-effectiveness, intention to use Big Data technology, and the need for Big Data technology. This data was collected from large businesses organization in the UAE, with a sample size of 250. After data cleaning and removing missing values, the sample size becomes 204. Binary logistic regression was used to analyze the data.Results showed that the model involving six independent variables was statistically significant for predicting cloud computing adoption with an accuracy of 90.6%. Cloud computing adoption can only be predicted independently by its usefulness. In this study, we found that CCA can be driven by a combination of six independent variables. The findings of this study are valuable for decision-making managers considering the adoption of cloud computing.