The growing popularity of cloud computing has increased demands of application migration to the cloud, but among others, the main deterrent has been the complexity of the migration planning for the large-scale projects with 1000s of servers. The complexity is mainly derived from the plethora of different migration options and required platform/application changes for cloud fitness of applications. In addition, the automated or artificial intelligence planning has been used to generate the migration plans, but despite its effectiveness, it has not been adopted broadly in migration because the AI planning language is complex and hard to scale. In this paper, we propose Cloud Readiness Planning Tool (CRPT), a system that constitutes a Migration Type Classifier trained under a novel active learning strategy dealing with 'concept drift', and an AI Planner which generates plan from automatically created domain and problem files with declarative specifications such as goal states and data in user friendly input formats. A series of experiments were conducted on a real-world migration task. The results demonstrate the Migration Type Classifier is able to effectively adapt to the changing business needs and achieve high accuracy with low labeling cost.