Flexible Electronics (FE) technology offers unique characteristics in electronic manufacturing, providing ultra-lowcost, lightweight, and environmentally-friendly alternatives to traditional rigid electronics. These characteristics enable a range of applications that were previously constrained by the cost and rigidity of conventional silicon technology. Machine learning (ML) is essential for enabling autonomous, real-time intelligence on devices with smart sensing capabilities in everyday objects. However, the large feature sizes and high power consumption of the devices oppose a challenge in the realization of flexible ML applications. To address the above, we propose an open-source framework for developing ML co-processors for the Bendable RISC-V core. In addition, we present a custom ML accelerator architecture for Support Vector Machine (SVM), supporting both one-vs-one (OvO) and one-vs-rest (OvR) algorithms. Our ML accelerator adopts a generic, precision-scalable design, supporting 4-, 8 -, and 16 -bit weight representations. Experimental results demonstrate a $21 \times$ improvement in both inference execution time and energy efficiency, on average, highlighting its potential for low-power, flexible intelligence on the edge.