A computing system for facilitating programming settings of an implantable pulse generator associated with a patient, comprising: a processor; a memory; and pulse generator feedback control logic, stored in the memory, and configured, when executed by the processor, to interface with control instructions of the implantable pulse generator by performing actions of: a) incorporating one or more machine learning engines, automatically generating a proposed set of stimulation parameter values that each affect a stimulation aspect of the IPG; b) forwarding the automatically generated proposed set of stimulation parameter values to configure stimulation parameters of the IPG to the proposed set of stimulation parameter values; c) receiving one or more clinical response values as a result of the IPG being configured to the proposed set of stimulation parameter values; characterised in that said pulse generator feedback control logic is further configured for d) predicting, by incorporating one or more machine learning engines and using the one or more clinical response values, at least one therapeutic response value for each of a plurality of sets of untested stimulation parameter values; e) selecting, based on the predicted therapeutic response values and on a distance of each set of untested stimulation parameter values from one or more previously tested stimulation parameter values, a revised proposed set of stimulation parameter values from the plurality of sets of untested stimulation parameter values; f) forwarding the revised proposed set to configure stimulation parameters of the IPG to the revised proposed set of stimulation parameter values; and g) repeating actions c) to f) until or unless a stop condition has been reached or the one or more received clinical response values indicates a value that corresponds to a therapeutic response indication within a designated tolerance. a processor; a memory; and pulse generator feedback control logic, stored in the memory, and configured, when executed by the processor, to interface with control instructions of the implantable pulse generator by performing actions of: a) incorporating one or more machine learning engines, automatically generating a proposed set of stimulation parameter values that each affect a stimulation aspect of the IPG; b) forwarding the automatically generated proposed set of stimulation parameter values to configure stimulation parameters of the IPG to the proposed set of stimulation parameter values; c) receiving one or more clinical response values as a result of the IPG being configured to the proposed set of stimulation parameter values; characterised in that said pulse generator feedback control logic is further configured for d) predicting, by incorporating one or more machine learning engines and using the one or more clinical response values, at least one therapeutic response value for each of a plurality of sets of untested stimulation parameter values; e) selecting, based on the predicted therapeutic response values and on a distance of each set of untested stimulation parameter values from one or more previously tested stimulation parameter values, a revised proposed set of stimulation parameter values from the plurality of sets of untested stimulation parameter values; f) forwarding the revised proposed set to configure stimulation parameters of the IPG to the revised proposed set of stimulation parameter values; and g) repeating actions c) to f) until or unless a stop condition has been reached or the one or more received clinical response values indicates a value that corresponds to a therapeutic response indication within a designated tolerance. a) incorporating one or more machine learning engines, automatically generating a proposed set of stimulation parameter values that each affect a stimulation aspect of the IPG; b) forwarding the automatically generated proposed set of stimulation parameter values to configure stimulation parameters of the IPG to the proposed set of stimulation parameter values; c) receiving one or more clinical response values as a result of the IPG being configured to the proposed set of stimulation parameter values; characterised in that said pulse generator feedback control logic is further configured for d) predicting, by incorporating one or more machine learning engines and using the one or more clinical response values, at least one therapeutic response value for each of a plurality of sets of untested stimulation parameter values; e) selecting, based on the predicted therapeutic response values and on a distance of each set of untested stimulation parameter values from one or more previously tested stimulation parameter values, a revised proposed set of stimulation parameter values from the plurality of sets of untested stimulation parameter values; f) forwarding the revised proposed set to configure stimulation parameters of the IPG to the revised proposed set of stimulation parameter values; and g) repeating actions c) to f) until or unless a stop condition has been reached or the one or more received clinical response values indicates a value that corresponds to a therapeutic response indication within a designated tolerance.