Ion-sensors play a major role in physiology and healthcare monitoring since they are capable of continuously collecting biological data from body fluids. Nevertheless, ion interference from background electrolytes present in the sample is a paramount challenge for a precise multi-ion-monitoring. In this work, we propose the first system combining a battery-powered portable multi-channel electronic front-end, and an embedded Multi-output Support Vector Regressor (M-SVR), that supplies an accurate, continuous, and real-time monitoring of sodium, potassium, ammonium, and calcium ions. These are typical analytes tracked during physical exercise. The front-end interface was characterized through a sensor array built with screen-printed electrodes. Nernstian sensitivity and limit of detection comparable to a bulky laboratory potentiometer were achieved in both water and artificial sweat. The multivariate calibration model was deployed on a Raspberry Pi where the activity of the target ions were locally computed. The M-SVR model was trained, optimized, and tested on an experimental dataset acquired following a design of experiments. We demonstrate that the proposed multivariate regressor is a compact, low-complexity, accurate, and unbiased estimator of sodium and potassium ions activity. A global normalized root mean-squared error improvement of 6.97% , and global mean relative error improvement of 10.26% , were achieved with respect to a standard Multiple Linear Regressor (MLR). Within a real-time multi-ion-monitoring task, the overall system enabled the continuous monitoring and accurate determination of the four target ions activity, with an average accuracy improvement of 27.73% compared to a simple MLR, and a prediction latency of 22.68±1.73μs.