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Spinel ferrite exhibits large magnetocaloric effect that promotes its candidature for solid state magnetic refrigeration technology. This method of cooling is promising and can conveniently replace the gas-compression method of refrigeration due to its energy saving capacity and ecological cleanliness. Among the available magnetocaloric materials, spinel ferrite metal oxide enjoys low production cost, non-toxicity, ease of preparation and non-oxidative. However, effective utilization of this magnetocaloric material for cooling technology requires determination of its magnetic cooling efficiency which is subjected to time consuming, intensive and laborious experimental procedures. This work employs the applied magnetic field, ionic radii and the concentrations of spinel ferrite constituents to develop a genetically hybridized support vector regression model (G-SVR) and extreme learning machine (ELM) intelligent model …