This paper presents EDICT, a tool for simulating Edge interactions in IoT-enhanced environments. Recently, ML and AI-based techniques have gained prominence to solve IoT related challenges. However, such models require large and diverse datasets to perform well. Finding real-world datasets that capture the performance of IoT systems is a challenging task due to the cost of deploying devices and instrumenting environments, as well as privacy/security concerns. This task becomes more challenging when datasets for specific situations (e.g., overloaded system, emergency scenarios) are needed. EDICT enables IoT systems designers to evaluate the performance of their IoT systems at design time. EDICT is capable of generating performance metrics datasets for specific instances of IoT-enhanced environments under different configuration parameters. To support runtime adaptation of smart environments, EDICT enables rapid performance prediction using ML techniques.