The global transition to electric vehicles brings a fundamental challenge: drivers remain uncertain about whether their vehicle has enough energy to complete a journey. This range anxiety stems from three interconnected problems: unpredictable charging times, inaccurate energy consumption estimates, and the lack of intelligent systems that jointly optimize routing and charging while accounting for long-term battery health. This thesis addresses these challenges progressively. We first develop a hybrid physics-based and reinforcement learning framework for charging time prediction, capturing the nonlinear CC-CV charging dynamics and how they evolve as a battery ages. We then develop a physics-informed residual learning model for energy consumption prediction, revealing that driving behavior alone accounts for up to 79% variation for identical hardware. Finally, we integrate both models into a unified joint routing and charging optimization framework with an explicit driver-advisory output layer, producing three actionable per-trip recommendations: a route with charging waypoints, a per-stop charging target calibrated to the battery's current health and charger power, and a driving-mode recommendation. Two non-intuitive findings emerge. First, a closed-form detour threshold showing when routing to a non-nearest charger is simultaneously faster, cheaper, and less damaging to the battery. Second, the optimal charging ceiling is a non-linear function of both battery health and charger power, making the common 80% heuristic suboptimal for any battery below 95% of its original capacity. The result is a physically grounded, data-efficient system that treats EV energy management as a single interconnected challenge rather than a collection of isolated problems.