Typically a Neural Networks (NN) is trained on data centers using historic datasets, then a C source file (model as a char array) of the trained model is generated and flashed on IoT devices. This standard process impedes the flexibility of billions of deployed ML-powered devices as they cannot learn unseen/fresh data patterns (static intelligence) and are impossible to adapt to dynamic scenarios. Currently, to address this issue, Online Machine Learning (OL) algorithms are deployed on IoT devices that provide devices the ability to locally re-train themselves - continuously updating the last few NN layers using unseen data patterns encountered after deployment. In OL, catastrophic forgetting is common when NNs are trained using non-stationary data distribution. The majority of recent work in the OL domain embraces the implicit assumption that the distribution of local training data is balanced. But the fact is, the sensor data streams in real-world IoT are severely imbalanced and temporally correlated. This paper introduces Imbal-OL, a resource-friendly technique that can be used as an OL plugin to balance the size of classes in a range of data streams. When Imbal-OL processed stream is used for OL, the models can adapt faster to changes in the stream while parallelly preventing catastrophic forgetting. Experimental evaluation of Imbal-OL using CIFAR datasets over ResNet-18 demonstrates its ability to deal with imperfect data streams, as it manages to produce high-quality models even under challenging learning settings