Mobile processors are now capable of running complex machine learning tasks locally, yet our mobile operating systems often hold them back. To preserve battery life, heavy workloads are typically restricted to charging or idle periods, severely limiting time-sensitive applications such as real-time health monitoring or federated learning. While we need more innovative scheduling algorithms to overcome this, validating them on physical hardware is extremely difficult. Factors like thermal throttling, background kernel activity, and battery degradation create an unpredictable environment where no two tests are ever quite the same. To solve this reproducibility challenge, we introduce a new simulation framework. Instead of relying on inconsistent test runs on physical devices, our system records a device's natural baseline activity and mathematically superimposes the resource footprint of a heavy task. It allows us to model CPU saturation, energy loss, memory pressure, and thermal dynamics in a controlled software environment. When compared against ground-truth recordings from Samsung Galaxy S10e and Fold 5 devices, the simulator achieved correlation scores exceeding 0.90 for computing and thermal metrics. By isolating the workload's impact from environmental noise, this platform provides a scalable method for benchmarking neural network schedulers without the logistical bottlenecks associated with continuous hardware testing.