The quay crane assignment and scheduling problem (QCASP) involves determining the number of quay cranes (QCs) to be assigned to a container vessel and establishing detailed task schedules for each crane. The QCASP is classified as an NP-hard problem, and population-based stochastic algorithms, such as genetic algorithms (GA), are commonly employed to search for optimal solutions. However, solving such a complex problem using a GA typically requires the generation and evaluation of several chromosomes, which can significantly reduce the efficiency of the search process. This study proposes a machine learning (ML)-based method for generating the initial population to address this issue. Specifically, to reduce the search space, a clustering method is introduced for tasks based on the vessel’s stowage plan, and an initial solution generation rule is formulated by transforming the problem into a traveling salesman problem. Subsequently, the initial population is evaluated, and ML techniques are employed to learn and select high-quality solutions. The proposed approach enhances search efficiency through strategic initial solution generation while also yielding practically feasible QC task assignments.