The mobile phone industry, one of the fastest advancing sectors in production over the last few decades, has been associated with a high e-waste generation rate. Simultaneously, a high demand for the production of new electronic equipment has led to the scarcity of certain metals. In this context, many recent studies have focused on recovering certain metals from e-waste through the use of bioprocesses. Such recovery processes are based on the action of microorganisms that produce Fe(III) as an oxidant, in order to leach the copper contained in printed circuit boards. During the oxidation-reduction reaction between Fe(III) and metallic Cu, the color of the solution evolves from an initial reddish color, due to Fe(III), to a bluish-green color, due to the oxidized Cu. In this work, a hardware-software prototype is developed, through which the concentrations of the key analytes—Fe(III) and Cu(II)—can be determined in real time by monitoring the color of the solution. This is achieved through the use of a non-invasive system, taking into account the aggressiveness of the solutions used for the bioprocessing of electronic components. In the work presented herein, the evolution of the solution color during the bioprocessing of two different types of waste (i.e., electric cable and mobile phones) is analyzed and then compared with the results obtained for pure metallic copper. The results are validated through comparison of the predicted results with the outcomes of conventional procedures, including offline sampling and analysis of Cu(II) and Fe(III) through atomic absorption and UV-VIS spectroscopy, respectively. The developed monitoring system allows an algorithm to be designed that can fit the evolution of analyte concentrations without the need for sampling or the use of complex, tedious, and expensive analytic techniques. It is also worth noting that the monitoring system is not in direct contact with the solution (which is highly aggressive for the processing of electronic equipment), making the system more durable than classic sensors that must be submerged in the solution. The real-time nature of the obtained information allows for the development of control actions and for corrective measures to be taken without affecting the biomass involved in the process.