The increasing demand of power and the need to reduce our dependency on fossil resources represent an opportunity to valorise low-to-medium grade heat streams such as mild hot streams from industry and natural brines into electricity. A systematic approach is required for the simultaneous selection of thermodynamic cycle which includes its configuration, the thermal fluid, and the optimal operating conditions. A methodology that integrates heuristics, for pre-screening, machine learning, to include rigorous thermodynamics, and mathematical optimization, for process flowsheet design is proposed. The pre-screening yields three fluids, benzene, toluene and 1,1,1,2,3,3,3-heptafluoropropane (R227ea) and two promising cycles, dual pressure organic Rankine cycle (ORC) and organic flash Rankine cycle (OFRC). The mathematical optimization shows that for temperatures over 120 °C, the OFRC using Benzene is the configuration of choice in terms of thermodynamic performance, but the ORC provides the most economical electricity. For hot resources below 120 °C, the efficiency of both cycles converges, but the best fluid turns out to be R227ea alongside the dual ORC cycle showing better performance and lower cost. The cooling costs present a minimum at ΔTmin equal to 8 °C. The results on process design are used to evaluate a exploitation of geothermal resources across Spain.