Compared to other social media data, email data differs from it in various topic-specific ways, including extensive replies, formal language, significant length disparities, high levels of anomalies, and indirect linkages. In this paper, the creation of a potent and computationally effective classifier to categorize spam and ham email documents is proposed. To assess and validate spam texts, this paper employs a variety of data mining-based classification approaches. On the benchmark Enron dataset, which is open to the public, tests were run. The final 7 Enron datasets were created by combining the six different types of Enron datasets that we had acquired. We preprocess the dataset at an early stage to exclude any useless phrases. This method falls under several categories, including Logistic Regression (LR), Convolutional Neural Networks (CNN), Random Forests (RF), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and suggested Deep Neural Networks (DNN). Using Bidirectional Long Short-Term Memory (BiLSTM), email documents may be screened for spam and labeled as such. In performance comparisons, DNN-BiLSTM outperforms other classifiers in terms of accuracy on all seven Enron datasets. In comparison to other machine learning classifiers, the findings demonstrate that DNN-BiLSTM and Convolutional Neural Networks can categorize spam with 96.39 % and 98.69 % accuracy, respectively. The report also covers the dangers of managing cloud data and the security problems that might occur. To safeguard data in the cloud while maintaining privacy, hybrid encryption is examined in this white paper. In the AES-Rabit hybrid encryption system, the symmetric session key exchange-based Rabit technique is combined with the benefits of the AES algorithm for faster data encryption.