A master's thesis titled "Global Optimization for PV-Integrated Smart Distribution Grids Using Data-Driven Neural Networks," supervised by Dr. Raheel Zafar and evaluated by Dr. Nauman Zafar Butt. The core idea is a teacher-student setup: an SOCP relaxation acts as a convex "teacher" that produces near-optimal solutions to a voltage-var optimization (VVO) problem, and a feedforward neural network (FCNN) is trained as a fast "student" surrogate that mimics the teacher at inference speed. A constraint-embedding trick using a tanh-bounded output layer cut infeasibility rates from 76.61% down to 0.13%. Validated on the IEEE 33-bus system using OpenDSS, with cyclical time encoding to capture daily/seasonal patterns in the grid data.