Abstract: Systems and methods are provided that that may be implemented to perform generalized adaptive storage endpoint prediction. A system/storage management application gathers data samples pertaining to operation of a storage device (e.g., remaining rated write endurance, available spare blocks, remaining drive space), clusters the data samples (e.g., using DBSCAN algorithm), and approximates a polynomial function usable to predict an endpoint of the storage device (e.g., SSD) by using an artificial neural network that receives the data samples and clusters and in response generates the approximated polynomial function. The approximate polynomial function may be a combination of Gaussian functions corresponding to the clusters. A mean and variance associated with each cluster may be calculated that is a mean and variance of a corresponding Gaussian function. A feed forward artificial neural network having at least one hidden layer, constant bias, and Gaussian activation function may be employed.