The SCIP Optimization Suite 9.0 is a collection of software packages for mathematical optimization, centered around the constraint integer programming (CIP) framework SCIP. This report discusses the enhancements and extensions in SCIP 9.0, including improved symmetry handling, new nonlinear handlers, a new cut generator, and two new cut selection schemes. SCIP 9.0 also features new interfaces for Rust and C++ and a new Python interface for SoPLEX. The report also highlights improvements in the LP solver SoPLEX, the presolving library PAPILO, the parallel framework UG, the decomposition framework GCG, and the SCIP extension SCIP-SDP. These updates result in improved performance in terms of solving time, number of nodes in the branch-and-bound tree, and solver reliability. SCIP is designed as a solver for constraint integer programs (CIPs), a generalization of mixed-integer linear and nonlinear programs. CIPs are finite-dimensional optimization problems with arbitrary constraints and a linear objective function. SCIP can be extended by plugins for more general or problem-specific classes of optimization problems. The core of SCIP is formed by a central branch-cut-and-price algorithm that utilizes an LP as the default relaxation. SCIP interacts closely with other components of the SCIP Optimization Suite, including ZIMPL, PAPILO, GCG, and UG. SCIP 9.0 introduces new features such as improved symmetry handling, new nonlinear handlers, and a new cut generator. It also includes new interfaces for Rust and C++ and a new Python interface for SoPLEX. The report details performance improvements in MILP and MINLP instances, with SCIP 9.0 solving more instances and improving performance in terms of solving time and number of nodes. The report also discusses improvements in symmetry handling, nonlinear handlers, primal heuristics, and cutting planes. These updates contribute to an overall improvement in SCIP's performance and reliability.The SCIP Optimization Suite 9.0 is a collection of software packages for mathematical optimization, centered around the constraint integer programming (CIP) framework SCIP. This report discusses the enhancements and extensions in SCIP 9.0, including improved symmetry handling, new nonlinear handlers, a new cut generator, and two new cut selection schemes. SCIP 9.0 also features new interfaces for Rust and C++ and a new Python interface for SoPLEX. The report also highlights improvements in the LP solver SoPLEX, the presolving library PAPILO, the parallel framework UG, the decomposition framework GCG, and the SCIP extension SCIP-SDP. These updates result in improved performance in terms of solving time, number of nodes in the branch-and-bound tree, and solver reliability. SCIP is designed as a solver for constraint integer programs (CIPs), a generalization of mixed-integer linear and nonlinear programs. CIPs are finite-dimensional optimization problems with arbitrary constraints and a linear objective function. SCIP can be extended by plugins for more general or problem-specific classes of optimization problems. The core of SCIP is formed by a central branch-cut-and-price algorithm that utilizes an LP as the default relaxation. SCIP interacts closely with other components of the SCIP Optimization Suite, including ZIMPL, PAPILO, GCG, and UG. SCIP 9.0 introduces new features such as improved symmetry handling, new nonlinear handlers, and a new cut generator. It also includes new interfaces for Rust and C++ and a new Python interface for SoPLEX. The report details performance improvements in MILP and MINLP instances, with SCIP 9.0 solving more instances and improving performance in terms of solving time and number of nodes. The report also discusses improvements in symmetry handling, nonlinear handlers, primal heuristics, and cutting planes. These updates contribute to an overall improvement in SCIP's performance and reliability.