This book presents a comprehensive collection of solved exercises in probability theory, intended for students, educators, and practitioners in the fields of mathematics, statistics, and related disciplines. The work covers both discrete and continuous probability distributions, exploring fundamental concepts such as random variables, expectation, variance, probability laws, conditional probability, independence, and the central limit theorem. Through a series of carefully structured problems accompanied by rigorous solutions, readers are introduced to classical distributions including binomial, geometric, Poisson, uniform, exponential, and normal distributions. Each chapter is designed to strengthen conceptual understanding and to develop analytical proficiency through application-driven scenarios such as quality control, random sampling, reliability modeling, and statistical inference. In addition to core topics, the book integrates advanced problem-solving strategies, approximation techniques (e.g., normal approximation to the binomial distribution), and real-world applications that bridge theoretical concepts with practical relevance.