Deep brain stimulation (DBS) is a therapy with promissory results for the treatment of movement disorders. It delivers electric stimulation via an electrode to a specific target brain region. The spatial extent of neural response to this stimulation is known as volume of tissue activated (VTA). Changes in stimulation parameters that control VTA, such as amplitude, pulse width and electrode configuration can affect the effectiveness of the DBS therapy. In this study, we develop a novel methodology for estimating suitable DBS neuromodulation parameters, from planned VTA, that attempts to maximize the therapeutic effects and to minimize the adverse effects for a patient in treatment. For estimating the continuous outputs (amplitude and pulse width), we use multi-output support vector regression, taking as the input space, the geometry of the VTA. For estimating the electrode polarity configuration, we perform several classification problems, also using support vector machines from the same input space. Our methodology attains satisfactory results for both the regression setting, and for predicting active contacts and its polarity. Combining biological neural modeling techniques together with machine learning, we introduce a promising area of research where parameters of neuromodulation in DBS can be tuned by manually specifying a desired geometric volume.