This chapter examines Hill Climbing, a fundamental optimization technique in artificial intelligence. We present both theoretical foundations and algorithm implementations. Several variants receive detailed treatment, including Steepest Ascent, Stochastic, and Random-Restart approaches. The material highlights each method’s strengths and limitations. Python implementations demonstrate practical applications to various problems.