We present a machine learning approach to predict the strategy behind the optimal solution of continuous and mixed-integer convex optimization problems. Using interpretable algorithms such as optimal classification trees we gain insights on the relationship between the problem data and the optimal solution. In this way, optimization is no longer a black-box and practitioners can understand it. Moreover, our method is able to compute the optimal solutions at very high speed. This applies also to non-interpretable machine learning techniques such as neural networks since they can be evaluated very efficiently. We benchmark our approach on several examples obtaining accuracy above 90% and computation times multiple orders of magnitude faster than state-of-the-art solvers. Therefore, our method provides on the one hand a novel insightful understanding of the optimal strategies to solve a broad class of continuous and mixed-integer optimization problems and on the other hand a powerful computational tool to solve online optimization at very high speed.