The increasing availability of condition-monitoring data for components/systems has incentivized, in the past years, the development of data-driven Remaining Useful Life (RUL) prognostics algorithms for assets such as Wind Turbines. This Thesis aims to support, through a critical analysis, the choice of the best Open-Source Datasets for Wind Turbines for Prognostic and Health monitoring (PHM) and for predictive maintenance planning, and to develop a data-driven Machine Leaning model for RUL prognostics. The first part of this Thesis is divided into three sections. The first section provides an overview of the main characteristics of Open-Source Datasets compared by time span, sampling rate, number and type of parameters and type of components.