A major drawback of traditional Cox Proportional Hazard (CPH) technique for modeling survival data is its inability to identify and classify subjects based on their inherent biological associations with respective risk endpoints as well as its inability to model the possible interaction effects among the covariates. To this end, this work aims at developing some techniques for modeling survival time data, especially with competing risks to address these challenges. The objectives of this studyare to: (i) develop survival tree-based technique via the Classification and Regression Tree (CART) using within-node homogeneity and between-node heterogeneity methods; (ii) extend the Multi-Layer Perceptron Artificial Neural Network (MLPANN) for modeling survival data with competing risks; (iii) extend the Multivariate Adaptive Regression Splines (MARS) method to model classical survival data using Cox-Snell residuals; and (iv) compare the efficiencies of the proposed methods with the existing methods using simulated and real life data sets. rnThe survival trees were formed by calculating the reduction in impurity going from the parent node to the child nodes via an impurity function. Two impurity functions of Deviance and Cox-Snell Residuals were used for both the sum of squares and absolute value impurity functions in within-node homogeneity. The Cox-Snell Residual was employed as a common response in MLPANN and MARS models. For the between-node heterogeneity trees, different inference procedures for testing the equality of two cumulative incidence functions were employed. The efficiency of the various methods were assessed using root mean square, mean absolute and relative square errors.rnFindings of the study were that:rn(i) the within-node homogeneity and between-node heterogeneity survival Trees with competing risks were developed to model the data and classify the subjects based on their associated risks; rn(ii) a MLPANN-based method for modeling survival data with competing risks was developed; rn(iii) method for modeling classical survival data using MARS techniques was developed and was found capable of modeling the possible interactions among the covariates;rn(iv) the proposed survival trees model determined the cutpoint of the tree better than the existing methods;rn(v) the proposed MLPANN-based method was shown to be more efficient than the Cox PH model using three real life data sets; andrn(vi) results obtained based on simulated and real-life HIV data sets showed that the proposed survival MARS model was better than the existing methods considered.rnThis study concluded that the proposed methods were suitable for identifying subjects with their associated risks as well as being able to model possible interaction effects among the covariates on survival time. They are therefore recommended whenever interest is focused on identifying subjects with their biological risks through prognostic variable biomarkers for diagnostic purposes.