This study introduces a novel approach for predicting leukemia using integrated multi-omics technology combined with Machine Learning (ML) and Deep Learning (DL) techniques. The research aims to improve the accuracy of leukemia diagnosis by analyzing multi-omics data, including genomics, transcriptomics, and proteomics. The study compares various ML algorithms such as Random Forest (RF), Naive Bayes (NB), Decision Tree (DT), Logistic Regression (LR), and Gradient Boosting (GB) with DL methods like Recurrent Neural Networks (RNN) and Feedforward Neural Networks (FNN). The results show that GB achieved 97% accuracy in ML, while RNN outperformed with 98% accuracy in DL. The study emphasizes the importance of high-throughput technology in healthcare and highlights the potential of combining multiple omics datasets to enhance understanding and treatment of complex diseases. The proposed method effectively filters unclassified data and demonstrates the significance of DL in leukemia prediction. The study also discusses the implications of high-throughput technology in healthcare and offers improved patient care through personalized treatment options.This study introduces a novel approach for predicting leukemia using integrated multi-omics technology combined with Machine Learning (ML) and Deep Learning (DL) techniques. The research aims to improve the accuracy of leukemia diagnosis by analyzing multi-omics data, including genomics, transcriptomics, and proteomics. The study compares various ML algorithms such as Random Forest (RF), Naive Bayes (NB), Decision Tree (DT), Logistic Regression (LR), and Gradient Boosting (GB) with DL methods like Recurrent Neural Networks (RNN) and Feedforward Neural Networks (FNN). The results show that GB achieved 97% accuracy in ML, while RNN outperformed with 98% accuracy in DL. The study emphasizes the importance of high-throughput technology in healthcare and highlights the potential of combining multiple omics datasets to enhance understanding and treatment of complex diseases. The proposed method effectively filters unclassified data and demonstrates the significance of DL in leukemia prediction. The study also discusses the implications of high-throughput technology in healthcare and offers improved patient care through personalized treatment options.