Combining Smart Production Logistics (SPL) and Machine Learning(ML) for adaptive scheduling and execution of material handling may be criticalfor enhancing manufacturing competitiveness. SPL and ML may help identify,adapt, and respond to scheduling changes originating from disturbances in andenhance the operational performance of material handling. However, the literaturecombining SPL and ML for material handling is scarce. Accordingly, thepurpose of this study is to propose a framework applying ML for the dynamicscheduling and execution of material handling tasks in SPL. The study proposesan architecture including Cyber Physical System (CPS) and Internet of Things(IoT) applying ML for the dynamic scheduling and execution of material handling.Then, we describe the ML inputs, interactions, and work flow for realizingthe proposed architecture. Finally, the study presents digital services in a simulationenvironment exemplifying the dynamic scheduling and execution ofmaterial handling in SPL. The study concludes with essential implications to themanufacturing industry.