A method performed by a user equipment, UE, for optimizing processing of application requests, the method comprising receiving at least one application request initiated by at least one application; identifying whether the received at least one application request is multi-access edge computing, MEC, enabled; deciding a processing mode for the received at least one application request based on the received at least one application request being MEC enabled, wherein the processing mode is decided using at least one of the MEC and local computing; and performing at least one action based on the processing mode decided for the received at least one application request, wherein the deciding of the processing mode comprises: determining at least one parameter by accessing a device state system for the received at least one application request, wherein the at least one parameter includes at least one of signal strength of a radio access network, RAN, supported by the UE, type of the RAN, network congestion state associated with the RAN, a unique identifier, UID, of the at least one application associated with the received at least one application request; determining at least one of local processing time and edge processing time for the received at least one application request by passing the received at least one application request and the determined at least one parameter as a key value to at least one machine learning, ML, table; deriving a cost function for the received at least one application request; and generating a processing decision parameter based on analysis of the cost function and the determined at least one of local processing time and edge processing time for the received at least one application request, wherein the processing decision parameter indicates the processing mode for the received at least one application request. receiving at least one application request initiated by at least one application; identifying whether the received at least one application request is multi-access edge computing, MEC, enabled; deciding a processing mode for the received at least one application request based on the received at least one application request being MEC enabled, wherein the processing mode is decided using at least one of the MEC and local computing; and performing at least one action based on the processing mode decided for the received at least one application request, wherein the deciding of the processing mode comprises: determining at least one parameter by accessing a device state system for the received at least one application request, wherein the at least one parameter includes at least one of signal strength of a radio access network, RAN, supported by the UE, type of the RAN, network congestion state associated with the RAN, a unique identifier, UID, of the at least one application associated with the received at least one application request; determining at least one of local processing time and edge processing time for the received at least one application request by passing the received at least one application request and the determined at least one parameter as a key value to at least one machine learning, ML, table; deriving a cost function for the received at least one application request; and generating a processing decision parameter based on analysis of the cost function and the determined at least one of local processing time and edge processing time for the received at least one application request, wherein the processing decision parameter indicates the processing mode for the received at least one application request. determining at least one parameter by accessing a device state system for the received at least one application request, wherein the at least one parameter includes at least one of signal strength of a radio access network, RAN, supported by the UE, type of the RAN, network congestion state associated with the RAN, a unique identifier, UID, of the at least one application associated with the received at least one application request; determining at least one of local processing time and edge processing time for the received at least one application request by passing the received at least one application request and the determined at least one parameter as a key value to at least one machine learning, ML, table; deriving a cost function for the received at least one application request; and generating a processing decision parameter based on analysis of the cost function and the determined at least one of local processing time and edge processing time for the received at least one application request, wherein the processing decision parameter indicates the processing mode for the received at least one application request.