Virtual machine (VM) load prediction is a critical task in cloud computing. Accurate VM load prediction can help to improve resource utilization, reduce costs, and improve the quality of service. For the Hybrid LSTM and AdaBoost model, a novel approach is proposed for accurate VM load prediction in cloud environments. The proposed model combines the power of LSTM and AdaBoost model, aiming to capture temporal dependencies in the VM load data and enhance prediction accuracy. The proposed model leverages LSTM to learn patterns and dynamics from historical load data, while AdaBoost is used to create an ensemble of weak regressors that collectively make load predictions. The model follows a two-step process: first, LSTM is trained on historical load data to extract informative features, and then AdaBoost is trained to combine the predictions from multiple weak regressors. The hybrid model demonstrates improved performance in VM load prediction by effectively handling non-linear relationships, temporal dependencies, and complex load patterns. The outcome of the proposed model is calculated using metrics, i.e. MAE, MAPE, MSE, RMSE, R2, and compared with existing machine learning algorithms i.e., Adaboost, KNN, SVM and deep learning algorithms i.e. LSTM, RNN. The results clearly show the superiority of the proposed hybrid approach in accurately predicting virtual machine load, enabling efficient resource allocation and management in cloud computing environments.