The advancement in cancer research using high throughput technology and artificial intelligence is getting momentum to improve disease diagnosis and targeted therapy. However, the complex and imbalanced data with high dimensionality pose significant challenges for computational approaches and multi-omics data analysis. This study analyzes the overall survival probability using the Kaplan-Meier estimator and Cox proportional hazards regression model and utilizes the high-throughput ML-based ensemble methods to predict skin cancer. In this connection, researchers propose Machine Learning (ML) based ensemble techniques for skin cancer prediction and assess overall survival (OS) probability through Kaplan-Meier estimation and Cox hazard regression. Researchers used eight baseline classifiers namely Random Forest (RF), Decision Tree (DT), Gradient Boosting (GB), AdaBoost, Gaussian Naïve Bayes (GNB), Extra Tree (ET), Logistic Regression (LR), and Light Gradient Boosting Machine (Light GBM or LGBM). The study evaluated the performance of the proposed ensemble methods and survival analysis on Skin Cutaneous Melanoma Cancers (SKCM) using the Confusion Matrix (CM) and Receiver Operating Characteristic (ROC) curve. The proposed methods demonstrated promising results, outperforming other algorithms and models in terms of ROC (0.99) when compared to other traditional methods. Moreover, researchers created and trained 4 different ensemble methods (stacking, bagging, boosting, and voting) to achieve the best results. The performance was evaluated and interpreted using accuracy, precision, recall, F1 score, confusion matrix, and ROC curves respectively. Researchers have compared our proposed study with the existing state-of-the-art techniques. Thus, this research work contributes to diagnosing SKCM with high accuracy.