Infrared (IR) proximity sensors measure distance using either time-of-flight (ToF) or reflection intensity methods. While ToF offers higher precision, it requires costly, specialized components. Reflection based sensors use simpler circuits, enabling lower-cost designs. This study presents a multi-emitter reflection-intensity IR sensor as an economical alternative to near field object positioning. Six IR LEDs, sequentially driven, surround a central photodiode that captures backscattered signals. A machine-learning pipeline estimates object coordinates, cross section and height. Tested on 20 objects and 13,750 labeled data, the system achieved <1 cm mean positioning error, competitive to multi-zone ToF accuracy with reduced cost.